Extended-Kalman-Filter-CTRA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from scipy.stats import norm\n",
    "from sympy import Symbol, symbols, Matrix, sin, cos\n",
    "from sympy import init_printing\n",
    "from sympy.utilities.codegen import codegen\n",
    "init_printing(use_latex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extended Kalman Filter Implementation for Constant Turn Rate and Acceleration (CTRA) Vehicle Model in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Extended Kalman Filter Step](Extended-Kalman-Filter-Step.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Wikipedia](http://en.wikipedia.org/wiki/Extended_Kalman_filter) writes: In the extended Kalman filter, the state transition and observation models need not be linear functions of the state but may instead be differentiable functions.\n",
    "\n",
    "$\\boldsymbol{x}_{k} = g(\\boldsymbol{x}_{k-1}, \\boldsymbol{u}_{k-1}) + \\boldsymbol{w}_{k-1}$\n",
    "\n",
    "$\\boldsymbol{z}_{k} = h(\\boldsymbol{x}_{k}) + \\boldsymbol{v}_{k}$\n",
    "\n",
    "Where $w_k$ and $v_k$ are the process and observation noises which are both assumed to be zero mean Multivariate Gaussian noises with covariance matrix $Q$ and $R$ respectively.\n",
    "\n",
    "The function $g$ can be used to compute the predicted state from the previous estimate and similarly the function $h$ can be used to compute the predicted measurement from the predicted state. However, $g$ and $h$ cannot be applied to the covariance directly. Instead a matrix of partial derivatives (the Jacobian matrix) is computed.\n",
    "\n",
    "At each time step, the Jacobian is evaluated with current predicted states. These matrices can be used in the Kalman filter equations. This process essentially linearizes the non-linear function around the current estimate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Situation covered: You have a velocity sensor, which measures the vehicle speed ($v$) in heading direction ($\\psi$) and a yaw rate sensor ($\\dot \\psi$) which both have to fused with the position ($x$ & $y$) from a GPS sensor."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State Vector - Constant Turn Rate and Acceleration Vehicle Model (CTRA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Constant Turn Rate, Constant Velocity Model for a vehicle ![CTRV Model](CTRV-Model.png)\n",
    "\n",
    "$$x_k= \\left[ \\matrix{ x \\\\ y \\\\ \\psi \\\\ v \\\\ \\dot\\psi \\\\ a} \\right] = \\left[ \\matrix{ \\text{Position X} \\\\ \\text{Position Y} \\\\ \\text{Heading} \\\\ \\text{Velocity} \\\\ \\text{Yaw Rate} \\\\ \\text{longitudinal acceleration}} \\right]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "numstates=6 # States"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dt = 1.0/50.0 # Sample Rate of the Measurements is 50Hz\n",
    "dtGPS=1.0/10.0 # Sample Rate of GPS is 10Hz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All symbolic calculations are made with [Sympy](http://nbviewer.ipython.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-5-Sympy.ipynb). Thanks!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "vs, psis, dpsis, dts, xs, ys, lats, lons, axs = symbols('v \\psi \\dot\\psi T x y lat lon a')\n",
    "\n",
    "gs = Matrix([[xs + (1 / dpsis**2) * ((vs*dpsis + axs * dpsis * dts) * sin(psis + dpsis* dts) + axs * cos(psis + dpsis * dts) - vs * dpsis * sin(psis) - axs * cos(psis))],    \n",
    "             [ys + (1 / dpsis**2) * ((-vs*dpsis - axs * dpsis * dts) * cos(psis + dpsis* dts) + axs * sin(psis + dpsis * dts) + vs * dpsis * cos(psis) - axs * sin(psis))],\n",
    "             [psis+dpsis*dts],\n",
    "             [axs*dts + vs],\n",
    "             [dpsis],\n",
    "             [axs]])\n",
    "state = Matrix([xs,ys,psis,vs,dpsis,axs])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dynamic Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This formulas calculate how the state is evolving from one to the next time step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}x + \\frac{- \\dot\\psi v \\sin{\\left(\\psi \\right)} - a \\cos{\\left(\\psi \\right)} + a \\cos{\\left(T \\dot\\psi + \\psi \\right)} + \\left(T \\dot\\psi a + \\dot\\psi v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}}\\\\y + \\frac{\\dot\\psi v \\cos{\\left(\\psi \\right)} - a \\sin{\\left(\\psi \\right)} + a \\sin{\\left(T \\dot\\psi + \\psi \\right)} + \\left(- T \\dot\\psi a - \\dot\\psi v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}}\\\\T \\dot\\psi + \\psi\\\\T a + v\\\\\\dot\\psi\\\\a\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡    -\\dot\\psi⋅v⋅sin(\\psi) - a⋅cos(\\psi) + a⋅cos(T⋅\\dot\\psi + \\psi) + (T⋅\\dot\\\n",
       "⎢x + ─────────────────────────────────────────────────────────────────────────\n",
       "⎢                                                                 2           \n",
       "⎢                                                         \\dot\\psi            \n",
       "⎢                                                                             \n",
       "⎢    \\dot\\psi⋅v⋅cos(\\psi) - a⋅sin(\\psi) + a⋅sin(T⋅\\dot\\psi + \\psi) + (-T⋅\\dot\\\n",
       "⎢y + ─────────────────────────────────────────────────────────────────────────\n",
       "⎢                                                                 2           \n",
       "⎢                                                         \\dot\\psi            \n",
       "⎢                                                                             \n",
       "⎢                                                   T⋅\\dot\\psi + \\psi         \n",
       "⎢                                                                             \n",
       "⎢                                                        T⋅a + v              \n",
       "⎢                                                                             \n",
       "⎢                                                       \\dot\\psi              \n",
       "⎢                                                                             \n",
       "⎣                                                           a                 \n",
       "\n",
       "psi⋅a + \\dot\\psi⋅v)⋅sin(T⋅\\dot\\psi + \\psi)⎤\n",
       "──────────────────────────────────────────⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "psi⋅a - \\dot\\psi⋅v)⋅cos(T⋅\\dot\\psi + \\psi)⎥\n",
       "──────────────────────────────────────────⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎥\n",
       "                                          ⎦"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate the Jacobian of the Dynamic Matrix with respect to the state vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}x\\\\y\\\\\\psi\\\\v\\\\\\dot\\psi\\\\a\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡   x    ⎤\n",
       "⎢        ⎥\n",
       "⎢   y    ⎥\n",
       "⎢        ⎥\n",
       "⎢  \\psi  ⎥\n",
       "⎢        ⎥\n",
       "⎢   v    ⎥\n",
       "⎢        ⎥\n",
       "⎢\\dot\\psi⎥\n",
       "⎢        ⎥\n",
       "⎣   a    ⎦"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & \\frac{- \\dot\\psi v \\cos{\\left(\\psi \\right)} + a \\sin{\\left(\\psi \\right)} - a \\sin{\\left(T \\dot\\psi + \\psi \\right)} + \\left(T \\dot\\psi a + \\dot\\psi v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{- \\dot\\psi \\sin{\\left(\\psi \\right)} + \\dot\\psi \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{- T a \\sin{\\left(T \\dot\\psi + \\psi \\right)} + T \\left(T \\dot\\psi a + \\dot\\psi v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)} - v \\sin{\\left(\\psi \\right)} + \\left(T a + v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} - \\frac{2 \\left(- \\dot\\psi v \\sin{\\left(\\psi \\right)} - a \\cos{\\left(\\psi \\right)} + a \\cos{\\left(T \\dot\\psi + \\psi \\right)} + \\left(T \\dot\\psi a + \\dot\\psi v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)}\\right)}{\\dot\\psi^{3}} & \\frac{T \\dot\\psi \\sin{\\left(T \\dot\\psi + \\psi \\right)} - \\cos{\\left(\\psi \\right)} + \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}}\\\\0 & 1 & \\frac{- \\dot\\psi v \\sin{\\left(\\psi \\right)} - a \\cos{\\left(\\psi \\right)} + a \\cos{\\left(T \\dot\\psi + \\psi \\right)} - \\left(- T \\dot\\psi a - \\dot\\psi v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{\\dot\\psi \\cos{\\left(\\psi \\right)} - \\dot\\psi \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{T a \\cos{\\left(T \\dot\\psi + \\psi \\right)} - T \\left(- T \\dot\\psi a - \\dot\\psi v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)} + v \\cos{\\left(\\psi \\right)} + \\left(- T a - v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} - \\frac{2 \\left(\\dot\\psi v \\cos{\\left(\\psi \\right)} - a \\sin{\\left(\\psi \\right)} + a \\sin{\\left(T \\dot\\psi + \\psi \\right)} + \\left(- T \\dot\\psi a - \\dot\\psi v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)}\\right)}{\\dot\\psi^{3}} & \\frac{- T \\dot\\psi \\cos{\\left(T \\dot\\psi + \\psi \\right)} - \\sin{\\left(\\psi \\right)} + \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}}\\\\0 & 0 & 1 & 0 & T & 0\\\\0 & 0 & 0 & 1 & 0 & T\\\\0 & 0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡      -\\dot\\psi⋅v⋅cos(\\psi) + a⋅sin(\\psi) - a⋅sin(T⋅\\dot\\psi + \\psi) + (T⋅\\do\n",
       "⎢1  0  ───────────────────────────────────────────────────────────────────────\n",
       "⎢                                                                   2         \n",
       "⎢                                                           \\dot\\psi          \n",
       "⎢                                                                             \n",
       "⎢      -\\dot\\psi⋅v⋅sin(\\psi) - a⋅cos(\\psi) + a⋅cos(T⋅\\dot\\psi + \\psi) - (-T⋅\\d\n",
       "⎢0  1  ───────────────────────────────────────────────────────────────────────\n",
       "⎢                                                                   2         \n",
       "⎢                                                           \\dot\\psi          \n",
       "⎢                                                                             \n",
       "⎢0  0                                                           1             \n",
       "⎢                                                                             \n",
       "⎢0  0                                                           0             \n",
       "⎢                                                                             \n",
       "⎢0  0                                                           0             \n",
       "⎢                                                                             \n",
       "⎣0  0                                                           0             \n",
       "\n",
       "t\\psi⋅a + \\dot\\psi⋅v)⋅cos(T⋅\\dot\\psi + \\psi)   -\\dot\\psi⋅sin(\\psi) + \\dot\\psi⋅\n",
       "────────────────────────────────────────────   ───────────────────────────────\n",
       "                                                                             2\n",
       "                                                                     \\dot\\psi \n",
       "                                                                              \n",
       "ot\\psi⋅a - \\dot\\psi⋅v)⋅sin(T⋅\\dot\\psi + \\psi)  \\dot\\psi⋅cos(\\psi) - \\dot\\psi⋅c\n",
       "─────────────────────────────────────────────  ───────────────────────────────\n",
       "                                                                            2 \n",
       "                                                                    \\dot\\psi  \n",
       "                                                                              \n",
       "                                                                         0    \n",
       "                                                                              \n",
       "                                                                         1    \n",
       "                                                                              \n",
       "                                                                         0    \n",
       "                                                                              \n",
       "                                                                         0    \n",
       "\n",
       "sin(T⋅\\dot\\psi + \\psi)  -T⋅a⋅sin(T⋅\\dot\\psi + \\psi) + T⋅(T⋅\\dot\\psi⋅a + \\dot\\p\n",
       "──────────────────────  ──────────────────────────────────────────────────────\n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "os(T⋅\\dot\\psi + \\psi)   T⋅a⋅cos(T⋅\\dot\\psi + \\psi) - T⋅(-T⋅\\dot\\psi⋅a - \\dot\\p\n",
       "─────────────────────   ──────────────────────────────────────────────────────\n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "\n",
       "si⋅v)⋅cos(T⋅\\dot\\psi + \\psi) - v⋅sin(\\psi) + (T⋅a + v)⋅sin(T⋅\\dot\\psi + \\psi) \n",
       "───────────────────────────────────────────────────────────────────────────── \n",
       "               2                                                              \n",
       "       \\dot\\psi                                                               \n",
       "                                                                              \n",
       "si⋅v)⋅sin(T⋅\\dot\\psi + \\psi) + v⋅cos(\\psi) + (-T⋅a - v)⋅cos(T⋅\\dot\\psi + \\psi)\n",
       "──────────────────────────────────────────────────────────────────────────────\n",
       "               2                                                              \n",
       "       \\dot\\psi                                                               \n",
       "                                                                              \n",
       "                                                                        T     \n",
       "                                                                              \n",
       "                                                                        0     \n",
       "                                                                              \n",
       "                                                                        1     \n",
       "                                                                              \n",
       "                                                                        0     \n",
       "\n",
       "  2⋅(-\\dot\\psi⋅v⋅sin(\\psi) - a⋅cos(\\psi) + a⋅cos(T⋅\\dot\\psi + \\psi) + (T⋅\\dot\\\n",
       "- ────────────────────────────────────────────────────────────────────────────\n",
       "                                                                 3            \n",
       "                                                         \\dot\\psi             \n",
       "                                                                              \n",
       "   2⋅(\\dot\\psi⋅v⋅cos(\\psi) - a⋅sin(\\psi) + a⋅sin(T⋅\\dot\\psi + \\psi) + (-T⋅\\dot\n",
       " - ───────────────────────────────────────────────────────────────────────────\n",
       "                                                                  3           \n",
       "                                                          \\dot\\psi            \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "\n",
       "psi⋅a + \\dot\\psi⋅v)⋅sin(T⋅\\dot\\psi + \\psi))   T⋅\\dot\\psi⋅sin(T⋅\\dot\\psi + \\psi\n",
       "───────────────────────────────────────────   ────────────────────────────────\n",
       "                                                                              \n",
       "                                                                            \\d\n",
       "                                                                              \n",
       "\\psi⋅a - \\dot\\psi⋅v)⋅cos(T⋅\\dot\\psi + \\psi))  -T⋅\\dot\\psi⋅cos(T⋅\\dot\\psi + \\ps\n",
       "────────────────────────────────────────────  ────────────────────────────────\n",
       "                                                                              \n",
       "                                                                             \\\n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "\n",
       ") - cos(\\psi) + cos(T⋅\\dot\\psi + \\psi) ⎤\n",
       "────────────────────────────────────── ⎥\n",
       "      2                                ⎥\n",
       "ot\\psi                                 ⎥\n",
       "                                       ⎥\n",
       "i) - sin(\\psi) + sin(T⋅\\dot\\psi + \\psi)⎥\n",
       "───────────────────────────────────────⎥\n",
       "       2                               ⎥\n",
       "dot\\psi                                ⎥\n",
       "                                       ⎥\n",
       "   0                                   ⎥\n",
       "                                       ⎥\n",
       "   T                                   ⎥\n",
       "                                       ⎥\n",
       "   0                                   ⎥\n",
       "                                       ⎥\n",
       "   1                                   ⎦"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.jacobian(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & \\frac{- \\dot\\psi v \\cos{\\left(\\psi \\right)} + a \\sin{\\left(\\psi \\right)} - a \\sin{\\left(T \\dot\\psi + \\psi \\right)} + \\left(T \\dot\\psi a + \\dot\\psi v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{- \\dot\\psi \\sin{\\left(\\psi \\right)} + \\dot\\psi \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{- T a \\sin{\\left(T \\dot\\psi + \\psi \\right)} + T \\left(T \\dot\\psi a + \\dot\\psi v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)} - v \\sin{\\left(\\psi \\right)} + \\left(T a + v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} - \\frac{2 \\left(- \\dot\\psi v \\sin{\\left(\\psi \\right)} - a \\cos{\\left(\\psi \\right)} + a \\cos{\\left(T \\dot\\psi + \\psi \\right)} + \\left(T \\dot\\psi a + \\dot\\psi v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)}\\right)}{\\dot\\psi^{3}} & \\frac{T \\dot\\psi \\sin{\\left(T \\dot\\psi + \\psi \\right)} - \\cos{\\left(\\psi \\right)} + \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}}\\\\0 & 1 & \\frac{- \\dot\\psi v \\sin{\\left(\\psi \\right)} - a \\cos{\\left(\\psi \\right)} + a \\cos{\\left(T \\dot\\psi + \\psi \\right)} - \\left(- T \\dot\\psi a - \\dot\\psi v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{\\dot\\psi \\cos{\\left(\\psi \\right)} - \\dot\\psi \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} & \\frac{T a \\cos{\\left(T \\dot\\psi + \\psi \\right)} - T \\left(- T \\dot\\psi a - \\dot\\psi v\\right) \\sin{\\left(T \\dot\\psi + \\psi \\right)} + v \\cos{\\left(\\psi \\right)} + \\left(- T a - v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}} - \\frac{2 \\left(\\dot\\psi v \\cos{\\left(\\psi \\right)} - a \\sin{\\left(\\psi \\right)} + a \\sin{\\left(T \\dot\\psi + \\psi \\right)} + \\left(- T \\dot\\psi a - \\dot\\psi v\\right) \\cos{\\left(T \\dot\\psi + \\psi \\right)}\\right)}{\\dot\\psi^{3}} & \\frac{- T \\dot\\psi \\cos{\\left(T \\dot\\psi + \\psi \\right)} - \\sin{\\left(\\psi \\right)} + \\sin{\\left(T \\dot\\psi + \\psi \\right)}}{\\dot\\psi^{2}}\\\\0 & 0 & 1 & 0 & T & 0\\\\0 & 0 & 0 & 1 & 0 & T\\\\0 & 0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡      -\\dot\\psi⋅v⋅cos(\\psi) + a⋅sin(\\psi) - a⋅sin(T⋅\\dot\\psi + \\psi) + (T⋅\\do\n",
       "⎢1  0  ───────────────────────────────────────────────────────────────────────\n",
       "⎢                                                                   2         \n",
       "⎢                                                           \\dot\\psi          \n",
       "⎢                                                                             \n",
       "⎢      -\\dot\\psi⋅v⋅sin(\\psi) - a⋅cos(\\psi) + a⋅cos(T⋅\\dot\\psi + \\psi) - (-T⋅\\d\n",
       "⎢0  1  ───────────────────────────────────────────────────────────────────────\n",
       "⎢                                                                   2         \n",
       "⎢                                                           \\dot\\psi          \n",
       "⎢                                                                             \n",
       "⎢0  0                                                           1             \n",
       "⎢                                                                             \n",
       "⎢0  0                                                           0             \n",
       "⎢                                                                             \n",
       "⎢0  0                                                           0             \n",
       "⎢                                                                             \n",
       "⎣0  0                                                           0             \n",
       "\n",
       "t\\psi⋅a + \\dot\\psi⋅v)⋅cos(T⋅\\dot\\psi + \\psi)   -\\dot\\psi⋅sin(\\psi) + \\dot\\psi⋅\n",
       "────────────────────────────────────────────   ───────────────────────────────\n",
       "                                                                             2\n",
       "                                                                     \\dot\\psi \n",
       "                                                                              \n",
       "ot\\psi⋅a - \\dot\\psi⋅v)⋅sin(T⋅\\dot\\psi + \\psi)  \\dot\\psi⋅cos(\\psi) - \\dot\\psi⋅c\n",
       "─────────────────────────────────────────────  ───────────────────────────────\n",
       "                                                                            2 \n",
       "                                                                    \\dot\\psi  \n",
       "                                                                              \n",
       "                                                                         0    \n",
       "                                                                              \n",
       "                                                                         1    \n",
       "                                                                              \n",
       "                                                                         0    \n",
       "                                                                              \n",
       "                                                                         0    \n",
       "\n",
       "sin(T⋅\\dot\\psi + \\psi)  -T⋅a⋅sin(T⋅\\dot\\psi + \\psi) + T⋅(T⋅\\dot\\psi⋅a + \\dot\\p\n",
       "──────────────────────  ──────────────────────────────────────────────────────\n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "os(T⋅\\dot\\psi + \\psi)   T⋅a⋅cos(T⋅\\dot\\psi + \\psi) - T⋅(-T⋅\\dot\\psi⋅a - \\dot\\p\n",
       "─────────────────────   ──────────────────────────────────────────────────────\n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "\n",
       "si⋅v)⋅cos(T⋅\\dot\\psi + \\psi) - v⋅sin(\\psi) + (T⋅a + v)⋅sin(T⋅\\dot\\psi + \\psi) \n",
       "───────────────────────────────────────────────────────────────────────────── \n",
       "               2                                                              \n",
       "       \\dot\\psi                                                               \n",
       "                                                                              \n",
       "si⋅v)⋅sin(T⋅\\dot\\psi + \\psi) + v⋅cos(\\psi) + (-T⋅a - v)⋅cos(T⋅\\dot\\psi + \\psi)\n",
       "──────────────────────────────────────────────────────────────────────────────\n",
       "               2                                                              \n",
       "       \\dot\\psi                                                               \n",
       "                                                                              \n",
       "                                                                        T     \n",
       "                                                                              \n",
       "                                                                        0     \n",
       "                                                                              \n",
       "                                                                        1     \n",
       "                                                                              \n",
       "                                                                        0     \n",
       "\n",
       "  2⋅(-\\dot\\psi⋅v⋅sin(\\psi) - a⋅cos(\\psi) + a⋅cos(T⋅\\dot\\psi + \\psi) + (T⋅\\dot\\\n",
       "- ────────────────────────────────────────────────────────────────────────────\n",
       "                                                                 3            \n",
       "                                                         \\dot\\psi             \n",
       "                                                                              \n",
       "   2⋅(\\dot\\psi⋅v⋅cos(\\psi) - a⋅sin(\\psi) + a⋅sin(T⋅\\dot\\psi + \\psi) + (-T⋅\\dot\n",
       " - ───────────────────────────────────────────────────────────────────────────\n",
       "                                                                  3           \n",
       "                                                          \\dot\\psi            \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "\n",
       "psi⋅a + \\dot\\psi⋅v)⋅sin(T⋅\\dot\\psi + \\psi))   T⋅\\dot\\psi⋅sin(T⋅\\dot\\psi + \\psi\n",
       "───────────────────────────────────────────   ────────────────────────────────\n",
       "                                                                              \n",
       "                                                                            \\d\n",
       "                                                                              \n",
       "\\psi⋅a - \\dot\\psi⋅v)⋅cos(T⋅\\dot\\psi + \\psi))  -T⋅\\dot\\psi⋅cos(T⋅\\dot\\psi + \\ps\n",
       "────────────────────────────────────────────  ────────────────────────────────\n",
       "                                                                              \n",
       "                                                                             \\\n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "                                                                              \n",
       "\n",
       ") - cos(\\psi) + cos(T⋅\\dot\\psi + \\psi) ⎤\n",
       "────────────────────────────────────── ⎥\n",
       "      2                                ⎥\n",
       "ot\\psi                                 ⎥\n",
       "                                       ⎥\n",
       "i) - sin(\\psi) + sin(T⋅\\dot\\psi + \\psi)⎥\n",
       "───────────────────────────────────────⎥\n",
       "       2                               ⎥\n",
       "dot\\psi                                ⎥\n",
       "                                       ⎥\n",
       "   0                                   ⎥\n",
       "                                       ⎥\n",
       "   T                                   ⎥\n",
       "                                       ⎥\n",
       "   0                                   ⎥\n",
       "                                       ⎥\n",
       "   1                                   ⎦"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.jacobian(state)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has to be computed on every filter step because it consists of state variables!\n",
    "\n",
    "To Sympy Team: A `.to_python` and `.to_c` and `.to_matlab` whould be nice to generate code, like it already works with `print latex()`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Uncertainty $P_0$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialized with $0$ means you are pretty sure where the vehicle starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1000.    0.    0.    0.    0.    0.]\n",
      " [   0. 1000.    0.    0.    0.    0.]\n",
      " [   0.    0. 1000.    0.    0.    0.]\n",
      " [   0.    0.    0. 1000.    0.    0.]\n",
      " [   0.    0.    0.    0. 1000.    0.]\n",
      " [   0.    0.    0.    0.    0. 1000.]] (6, 6)\n"
     ]
    }
   ],
   "source": [
    "P = np.diag([1000.0, 1000.0, 1000.0, 1000.0, 1000.0, 1000.0])\n",
    "print(P, P.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process Noise Covariance Matrix Q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"*The state uncertainty model models the disturbances which excite the linear system. Conceptually, it estimates how bad things can get when the system is run open loop for a given period of time.*\" - Kelly, A. (1994). A 3D state space formulation of a navigation Kalman filter for autonomous vehicles, (May). Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA282853"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.0976e-06 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 3.0976e-06 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 4.0000e-06 0.0000e+00 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 0.0000e+00 3.0976e-02 0.0000e+00 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 4.0000e-04 0.0000e+00]\n",
      " [0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 2.5000e-01]] (6, 6)\n"
     ]
    }
   ],
   "source": [
    "sGPS     = 0.5*8.8*dt**2  # assume 8.8m/s2 as maximum acceleration, forcing the vehicle\n",
    "sCourse  = 0.1*dt # assume 0.1rad/s as maximum turn rate for the vehicle\n",
    "sVelocity= 8.8*dt # assume 8.8m/s2 as maximum acceleration, forcing the vehicle\n",
    "sYaw     = 1.0*dt # assume 1.0rad/s2 as the maximum turn rate acceleration for the vehicle\n",
    "sAccel   = 0.5\n",
    "\n",
    "Q = np.diag([sGPS**2, sGPS**2, sCourse**2, sVelocity**2, sYaw**2, sAccel**2])\n",
    "print(Q, Q.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5, 5))\n",
    "im = plt.imshow(Q, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Process Noise Covariance Matrix $Q$')\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(6),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$', '$a$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(6),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$', '$a$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,5.5])\n",
    "plt.ylim([5.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read '2014-03-26-000-Data.csv' successfully.\n"
     ]
    }
   ],
   "source": [
    "#path = './../RaspberryPi-CarPC/TinkerDataLogger/DataLogs/2014/'\n",
    "datafile = '2014-03-26-000-Data.csv'\n",
    "\n",
    "date, \\\n",
    "time, \\\n",
    "millis, \\\n",
    "ax, \\\n",
    "ay, \\\n",
    "az, \\\n",
    "rollrate, \\\n",
    "pitchrate, \\\n",
    "yawrate, \\\n",
    "roll, \\\n",
    "pitch, \\\n",
    "yaw, \\\n",
    "speed, \\\n",
    "course, \\\n",
    "latitude, \\\n",
    "longitude, \\\n",
    "altitude, \\\n",
    "pdop, \\\n",
    "hdop, \\\n",
    "vdop, \\\n",
    "epe, \\\n",
    "fix, \\\n",
    "satellites_view, \\\n",
    "satellites_used, \\\n",
    "temp = np.loadtxt(datafile, delimiter=',', unpack=True, \n",
    "                  #converters={1: mdates.strpdate2num('%H%M%S%f'),\n",
    "                  #            0: mdates.strpdate2num('%y%m%d')},\n",
    "                  skiprows=1)\n",
    "\n",
    "print('Read \\'%s\\' successfully.' % datafile)\n",
    "\n",
    "# A course of 0° means the Car is traveling north bound\n",
    "# and 90° means it is traveling east bound.\n",
    "# In the Calculation following, East is Zero and North is 90°\n",
    "# We need an offset.\n",
    "course =(-course+90.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measurement Function H"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matrix $J_H$ is the Jacobian of the Measurement function $h$ with respect to the state. Function $h$ can be used to compute the predicted measurement from the predicted state.\n",
    "\n",
    "If a GPS measurement is available, the following function maps the state to the measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}x\\\\y\\\\v\\\\\\dot\\psi\\\\a\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡   x    ⎤\n",
       "⎢        ⎥\n",
       "⎢   y    ⎥\n",
       "⎢        ⎥\n",
       "⎢   v    ⎥\n",
       "⎢        ⎥\n",
       "⎢\\dot\\psi⎥\n",
       "⎢        ⎥\n",
       "⎣   a    ⎦"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs = Matrix([[xs],\n",
    "             [ys],\n",
    "             [vs],\n",
    "             [dpsis],\n",
    "             [axs]])\n",
    "hs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle \\left[\\begin{matrix}1 & 0 & 0 & 0 & 0 & 0\\\\0 & 1 & 0 & 0 & 0 & 0\\\\0 & 0 & 0 & 1 & 0 & 0\\\\0 & 0 & 0 & 0 & 1 & 0\\\\0 & 0 & 0 & 0 & 0 & 1\\end{matrix}\\right]$"
      ],
      "text/plain": [
       "⎡1  0  0  0  0  0⎤\n",
       "⎢                ⎥\n",
       "⎢0  1  0  0  0  0⎥\n",
       "⎢                ⎥\n",
       "⎢0  0  0  1  0  0⎥\n",
       "⎢                ⎥\n",
       "⎢0  0  0  0  1  0⎥\n",
       "⎢                ⎥\n",
       "⎣0  0  0  0  0  1⎦"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "JHs=hs.jacobian(state)\n",
    "JHs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If no GPS measurement is available, simply set the corresponding values in $J_h$ to zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Measurement Noise Covariance $R$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"In practical use, the uncertainty estimates take on the significance of relative weights of state estimates and measurements. So it is not so much important that uncertainty is absolutely correct as it is that it be relatively consistent across all models\" - Kelly, A. (1994). A 3D state space formulation of a navigation Kalman filter for autonomous vehicles, (May). Retrieved from http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA282853"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.5e+01 0.0e+00 0.0e+00 0.0e+00 0.0e+00]\n",
      " [0.0e+00 2.5e+01 0.0e+00 0.0e+00 0.0e+00]\n",
      " [0.0e+00 0.0e+00 9.0e+00 0.0e+00 0.0e+00]\n",
      " [0.0e+00 0.0e+00 0.0e+00 1.0e-02 0.0e+00]\n",
      " [0.0e+00 0.0e+00 0.0e+00 0.0e+00 1.0e+00]] (5, 5)\n"
     ]
    }
   ],
   "source": [
    "varGPS = 5.0 # Standard Deviation of GPS Measurement\n",
    "varspeed = 3.0 # Variance of the speed measurement\n",
    "varyaw = 0.1 # Variance of the yawrate measurement\n",
    "varacc = 1.0 # Variance of the longitudinal Acceleration\n",
    "R = np.diag([varGPS**2, varGPS**2, varspeed**2, varyaw**2, varacc**2])\n",
    "\n",
    "print(R, R.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 324x324 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4.5, 4.5))\n",
    "im = plt.imshow(R, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Measurement Noise Covariance Matrix $R$')\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations of the yticks\n",
    "plt.yticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(5),('$x$', '$y$', '$v$', '$\\dot \\psi$', '$a$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "# set the locations of the yticks\n",
    "plt.xticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(5),('$x$', '$y$', '$v$', '$\\dot \\psi$', '$a$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,4.5])\n",
    "plt.ylim([4.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identity Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 0. 1.]] (6, 6)\n"
     ]
    }
   ],
   "source": [
    "I = np.eye(numstates)\n",
    "print(I, I.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Approx. Lat/Lon to Meters to check Location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "RadiusEarth = 6378388.0 # m\n",
    "arc= 2.0*np.pi*(RadiusEarth+altitude)/360.0 # m/°\n",
    "\n",
    "dx = arc * np.cos(latitude*np.pi/180.0) * np.hstack((0.0, np.diff(longitude))) # in m\n",
    "dy = arc * np.hstack((0.0, np.diff(latitude))) # in m\n",
    "\n",
    "mx = np.cumsum(dx)\n",
    "my = np.cumsum(dy)\n",
    "\n",
    "ds = np.sqrt(dx**2+dy**2)\n",
    "\n",
    "GPS=(ds!=0.0).astype('bool') # GPS Trigger for Kalman Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.        ]\n",
      " [ 0.        ]\n",
      " [-4.08756111]\n",
      " [ 0.67322222]\n",
      " [-0.32660346]\n",
      " [ 0.2647    ]] (6, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/latex": [
       "$\\displaystyle \\left( -0.055, \\  0.055, \\  -0.055, \\  0.055\\right)$"
      ],
      "text/plain": [
       "(-0.05500000000000001, 0.05500000000000001, -0.05500000000000001, 0.0550000000\n",
       "0000001)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.matrix([[mx[0], my[0], course[0]/180.0*np.pi, speed[0]/3.6+0.001, yawrate[0]/180.0*np.pi, ax[0]]]).T\n",
    "print(x, x.shape)\n",
    "\n",
    "U=float(np.cos(x[2])*x[3])\n",
    "V=float(np.sin(x[2])*x[3])\n",
    "\n",
    "plt.quiver(x[0], x[1], U, V)\n",
    "plt.scatter(float(x[0]), float(x[1]), s=100)\n",
    "plt.title('Initial Location')\n",
    "plt.axis('equal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Put everything together as a measurement vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 10800)\n"
     ]
    }
   ],
   "source": [
    "measurements = np.vstack((mx, my, speed/3.6, yawrate/180.0*np.pi, ax))\n",
    "# Lenth of the measurement\n",
    "m = measurements.shape[1]\n",
    "print(measurements.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Preallocation for Plotting\n",
    "x0 = []\n",
    "x1 = []\n",
    "x2 = []\n",
    "x3 = []\n",
    "x4 = []\n",
    "x5 = []\n",
    "Zx = []\n",
    "Zy = []\n",
    "Px = []\n",
    "Py = []\n",
    "Pdx= []\n",
    "Pdy= []\n",
    "Pddx=[]\n",
    "Pddy=[]\n",
    "Pdv =[]\n",
    "Kx = []\n",
    "Ky = []\n",
    "Kdx= []\n",
    "Kdy= []\n",
    "Kddx=[]\n",
    "Kdv= []\n",
    "dstate=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extended Kalman Filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Extended Kalman Filter Step](Extended-Kalman-Filter-Step.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$x_k= \\begin{bmatrix} x \\\\ y \\\\ \\psi \\\\ v \\\\ \\dot\\psi \\\\ a \\end{bmatrix} = \\begin{bmatrix} \\text{Position X} \\\\ \\text{Position Y} \\\\ \\text{Heading} \\\\ \\text{Velocity} \\\\ \\text{Yaw Rate} \\\\ \\text{acceleration} \\end{bmatrix} =  \\underbrace{\\begin{matrix}x[0] \\\\ x[1] \\\\ x[2] \\\\ x[3] \\\\ x[4] \\\\ x[5]  \\end{matrix}}_{\\textrm{Python Nomenclature}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "for filterstep in range(m):\n",
    "\n",
    "    # Time Update (Prediction)\n",
    "    # ========================\n",
    "    # Project the state ahead\n",
    "    # see \"Dynamic Matrix\"\n",
    "    if np.abs(yawrate[filterstep])<0.0001: # Driving straight\n",
    "        x[4] = 0.0001\n",
    "    x[0] = x[0] + (1 / x[4]**2) * ((x[3]*x[4] + x[5] * x[4] * dt) * \\\n",
    "        np.sin(x[2] + x[4]* dt) + x[5] * np.cos(x[2] + x[4] * dt) - x[3] *  \\\n",
    "        x[4] * np.sin(x[2]) - x[5] * np.cos(x[2]))\n",
    "    x[1] = x[1] + (1 / x[4]**2) * ((-x[3]*x[4] - x[5] * x[4] * dt) * \\\n",
    "        np.cos(x[2] + x[4]* dt) + x[5] * np.sin(x[2] + x[4] * dt) + x[3] * \\\n",
    "        x[4] * np.cos(x[2]) - x[5] * np.sin(x[2]))\n",
    "    x[2] = (x[2] + x[4] * dt + np.pi) % (2.0 * np.pi) - np.pi\n",
    "    x[3] = x[3] + x[5] * dt \n",
    "    x[4] = x[4]\n",
    "    x[5] = x[5]\n",
    "    \n",
    "    \n",
    "    # Calculate the Jacobian of the Dynamic Matrix A\n",
    "    # see \"Calculate the Jacobian of the Dynamic Matrix with respect to the state vector\"\n",
    "    a13 = ((-x[4]*x[3]*np.cos(x[2]) + x[5]*np.sin(x[2]) - x[5]*np.sin(dt*x[4] + x[2]) + \\\n",
    "        (dt*x[4]*x[5] + x[4]*x[3])*np.cos(dt*x[4] + x[2]))/x[4]**2).item(0)\n",
    "\n",
    "    a14 = ((-x[4]*np.sin(x[2]) + x[4]*np.sin(dt*x[4] + x[2]))/x[4]**2).item(0)\n",
    "\n",
    "    a15 = ((-dt*x[5]*np.sin(dt*x[4] + x[2]) + dt*(dt*x[4]*x[5] + x[4]*x[3])* \\\n",
    "        np.cos(dt*x[4] + x[2]) - x[3]*np.sin(x[2]) + (dt*x[5] + x[3])* \\\n",
    "        np.sin(dt*x[4] + x[2]))/x[4]**2 - 2*(-x[4]*x[3]*np.sin(x[2]) - x[5]* \\\n",
    "        np.cos(x[2]) + x[5]*np.cos(dt*x[4] + x[2]) + (dt*x[4]*x[5] + x[4]*x[3])* \\\n",
    "        np.sin(dt*x[4] + x[2]))/x[4]**3).item(0)\n",
    "\n",
    "    a16 = ((dt*x[4]*np.sin(dt*x[4] + x[2]) - np.cos(x[2]) + np.cos(dt * x[4] + x[2]))/x[4]**2).item(0)\n",
    "\n",
    "    a23 = ((-x[4] * x[3] * np.sin(x[2]) - x[5] * np.cos(x[2]) + x[5] * np.cos(dt * x[4] + x[2]) - \\\n",
    "        (-dt * x[4]*x[5] - x[4] * x[3]) * np.sin(dt * x[4] + x[2])) / x[4]**2).item(0)\n",
    "    a24 = ((x[4] * np.cos(x[2]) - x[4]*np.cos(dt*x[4] + x[2]))/x[4]**2).item(0)\n",
    "    a25 = ((dt * x[5]*np.cos(dt*x[4] + x[2]) - dt * (-dt*x[4]*x[5] - x[4] * x[3]) * \\\n",
    "        np.sin(dt * x[4] + x[2]) + x[3]*np.cos(x[2]) + (-dt*x[5] - x[3])*np.cos(dt*x[4] + x[2]))/ \\\n",
    "        x[4]**2 - 2*(x[4]*x[3]*np.cos(x[2]) - x[5] * np.sin(x[2]) + x[5] * np.sin(dt*x[4] + x[2]) + \\\n",
    "        (-dt * x[4] * x[5] - x[4] * x[3])*np.cos(dt*x[4] + x[2]))/x[4]**3).item(0)\n",
    "    a26 =  ((-dt*x[4]*np.cos(dt*x[4] + x[2]) - np.sin(x[2]) + np.sin(dt*x[4] + x[2]))/x[4]**2).item(0)\n",
    "        \n",
    "    JA = np.matrix([[1.0, 0.0, a13, a14, a15, a16],\n",
    "                    [0.0, 1.0, a23, a24, a25, a26],\n",
    "                    [0.0, 0.0, 1.0, 0.0, dt, 0.0],\n",
    "                    [0.0, 0.0, 0.0, 1.0, 0.0, dt],\n",
    "                    [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],\n",
    "                    [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])\n",
    "    \n",
    "    \n",
    "    # Project the error covariance ahead\n",
    "    P = JA*P*JA.T + Q\n",
    "    \n",
    "    # Measurement Update (Correction)\n",
    "    # ===============================\n",
    "    # Measurement Function\n",
    "    hx = np.matrix([[float(x[0])],\n",
    "                    [float(x[1])],\n",
    "                    [float(x[3])],\n",
    "                    [float(x[4])],\n",
    "                    [float(x[5])]])\n",
    "\n",
    "    if GPS[filterstep]: # with 10Hz, every 5th step\n",
    "        JH = np.matrix([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 1.0, 0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])\n",
    "    else: # every other step\n",
    "        JH = np.matrix([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],\n",
    "                        [0.0, 0.0, 0.0, 0.0, 0.0, 1.0]])        \n",
    "    \n",
    "    S = JH*P*JH.T + R\n",
    "    K = (P*JH.T) * np.linalg.inv(S)\n",
    "\n",
    "    # Update the estimate via\n",
    "    Z = measurements[:,filterstep].reshape(JH.shape[0],1)\n",
    "    y = Z - (hx)                         # Innovation or Residual\n",
    "    x = x + (K*y)\n",
    "\n",
    "    # Update the error covariance\n",
    "    P = (I - (K*JH))*P\n",
    "\n",
    "\n",
    "    # Save states for Plotting\n",
    "    x0.append(float(x[0]))\n",
    "    x1.append(float(x[1]))\n",
    "    x2.append(float(x[2]))\n",
    "    x3.append(float(x[3]))\n",
    "    x4.append(float(x[4]))\n",
    "    x5.append(float(x[5]))\n",
    "    Zx.append(float(Z[0]))\n",
    "    Zy.append(float(Z[1]))    \n",
    "    Px.append(float(P[0,0]))\n",
    "    Py.append(float(P[1,1]))\n",
    "    Pdx.append(float(P[2,2]))\n",
    "    Pdy.append(float(P[3,3]))\n",
    "    Pddx.append(float(P[4,4]))\n",
    "    Pdv.append(float(P[5,5]))\n",
    "    Kx.append(float(K[0,0]))\n",
    "    Ky.append(float(K[1,0]))\n",
    "    Kdx.append(float(K[2,0]))\n",
    "    Kdy.append(float(K[3,0]))\n",
    "    Kddx.append(float(K[4,0]))\n",
    "    Kdv.append(float(K[5,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncertainties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff9424dac70>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,9))\n",
    "plt.semilogy(range(m),Px, label='$x$')\n",
    "plt.step(range(m),Py, label='$y$')\n",
    "plt.step(range(m),Pdx, label='$\\psi$')\n",
    "plt.step(range(m),Pdy, label='$v$')\n",
    "plt.step(range(m),Pddx, label='$\\dot \\psi$')\n",
    "\n",
    "plt.xlabel('Filter Step')\n",
    "plt.ylabel('')\n",
    "plt.title('Uncertainty (Elements from Matrix $P$)')\n",
    "plt.legend(loc='best',prop={'size':22})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 6))\n",
    "im = plt.imshow(P, interpolation=\"none\", cmap=plt.get_cmap('binary'))\n",
    "plt.title('Covariance Matrix $P$ (after %i Filter Steps)' % (m))\n",
    "ylocs, ylabels = plt.yticks()\n",
    "# set the locations of the yticks\n",
    "plt.yticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.yticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "xlocs, xlabels = plt.xticks()\n",
    "# set the locations of the yticks\n",
    "plt.xticks(np.arange(6))\n",
    "# set the locations and labels of the yticks\n",
    "plt.xticks(np.arange(5),('$x$', '$y$', '$\\psi$', '$v$', '$\\dot \\psi$'), fontsize=22)\n",
    "\n",
    "plt.xlim([-0.5,4.5])\n",
    "plt.ylim([4.5, -0.5])\n",
    "\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "divider = make_axes_locatable(plt.gca())\n",
    "cax = divider.append_axes(\"right\", \"5%\", pad=\"3%\")\n",
    "plt.colorbar(im, cax=cax)\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kalman Gains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,9))\n",
    "plt.step(range(len(measurements[0])),Kx, label='$x$')\n",
    "plt.step(range(len(measurements[0])),Ky, label='$y$')\n",
    "plt.step(range(len(measurements[0])),Kdx, label='$\\psi$')\n",
    "plt.step(range(len(measurements[0])),Kdy, label='$v$')\n",
    "plt.step(range(len(measurements[0])),Kddx, label='$\\dot \\psi$')\n",
    "\n",
    "\n",
    "plt.xlabel('Filter Step')\n",
    "plt.ylabel('')\n",
    "plt.title('Kalman Gain (the lower, the more the measurement fullfill the prediction)')\n",
    "plt.legend(prop={'size':18})\n",
    "plt.ylim([-0.2,0.2]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State Vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,16))\n",
    "\n",
    "plt.subplot(511)\n",
    "plt.step(range(len(measurements[0])),x0-mx[0], label='$x$')\n",
    "plt.step(range(len(measurements[0])),x1-my[0], label='$y$')\n",
    "\n",
    "plt.title('Extended Kalman Filter State Estimates (State Vector $x$)')\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Position (relative to start) [m]')\n",
    "\n",
    "plt.subplot(512)\n",
    "plt.step(range(len(measurements[0])),x2, label='$\\psi$')\n",
    "plt.step(range(len(measurements[0])),(course/180.0*np.pi+np.pi)%(2.0*np.pi) - np.pi, label='$\\psi$ (from GPS as reference)')\n",
    "plt.ylabel('Course')\n",
    "plt.legend(loc='best')\n",
    "           \n",
    "plt.subplot(513)\n",
    "plt.step(range(len(measurements[0])),x3, label='$v$')\n",
    "plt.step(range(len(measurements[0])),speed/3.6, label='$v$ (from GPS as reference)', alpha=0.6)\n",
    "plt.ylabel('Velocity')\n",
    "plt.ylim([0, 30])\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(514)\n",
    "plt.step(range(len(measurements[0])),x4, label='$\\dot \\psi$')\n",
    "plt.step(range(len(measurements[0])),yawrate/180.0*np.pi, label='$\\dot \\psi$ (from IMU as reference)', alpha=0.6)\n",
    "plt.ylabel('Yaw Rate')\n",
    "plt.ylim([-0.6, 0.6])\n",
    "plt.legend(loc='best')\n",
    "\n",
    "plt.subplot(515)\n",
    "plt.step(range(len(measurements[0])),x5, label='$a$')\n",
    "plt.step(range(len(measurements[0])),ax, label='$a$ (from IMU as reference)', alpha=0.6)\n",
    "plt.ylabel('Acceleration')\n",
    "#plt.ylim([-0.6, 0.6])\n",
    "plt.legend(loc='best')\n",
    "plt.xlabel('Filter Step')\n",
    "\n",
    "plt.savefig('Extended-Kalman-Filter-CTRA-State-Estimates.png', dpi=72, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Position x/y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/latex": [
       "$\\displaystyle \\left( -38.6111231637142, \\  632.331009496849, \\  -26.2110916934331, \\  358.47730154143\\right)$"
      ],
      "text/plain": [
       "(-38.61112316371423, 632.3310094968492, -26.211091693433104, 358.4773015414300\n",
       "5)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,9))\n",
    "\n",
    "# EKF State\n",
    "plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "plt.plot(x0,x1, label='EKF Position', c='k', lw=5)\n",
    "\n",
    "# Measurements\n",
    "plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', marker='+')\n",
    "#cbar=plt.colorbar(ticks=np.arange(20))\n",
    "#cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "#cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "# Start/Goal\n",
    "plt.scatter(x0[0],x1[0], s=60, label='Start', c='g')\n",
    "plt.scatter(x0[-1],x1[-1], s=60, label='Goal', c='r')\n",
    "\n",
    "plt.xlabel('X [m]')\n",
    "plt.ylabel('Y [m]')\n",
    "plt.title('Position')\n",
    "plt.legend(loc='best')\n",
    "plt.axis('equal')\n",
    "#plt.tight_layout()\n",
    "\n",
    "#plt.savefig('Extended-Kalman-Filter-CTRA-Position.png', dpi=72, transparent=True, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Detailed View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff95731b460>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,9))\n",
    "\n",
    "plt.subplot(221)\n",
    "# EKF State\n",
    "#plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "plt.plot(x0,x1, label='EKF Position', c='g', lw=5)\n",
    "\n",
    "# Measurements\n",
    "plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', alpha=0.5, marker='+')\n",
    "#cbar=plt.colorbar(ticks=np.arange(20))\n",
    "#cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "#cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "plt.xlabel('X [m]')\n",
    "plt.xlim(70, 130)\n",
    "plt.ylabel('Y [m]')\n",
    "plt.ylim(140, 200)\n",
    "plt.title('Position')\n",
    "plt.legend(loc='best')\n",
    "\n",
    "\n",
    "plt.subplot(222)\n",
    "\n",
    "# EKF State\n",
    "#plt.quiver(x0,x1,np.cos(x2), np.sin(x2), color='#94C600', units='xy', width=0.05, scale=0.5)\n",
    "plt.plot(x0,x1, label='EKF Position', c='g', lw=5)\n",
    "\n",
    "# Measurements\n",
    "plt.scatter(mx[::5],my[::5], s=50, label='GPS Measurements', alpha=0.5, marker='+')\n",
    "#cbar=plt.colorbar(ticks=np.arange(20))\n",
    "#cbar.ax.set_ylabel(u'EPE', rotation=270)\n",
    "#cbar.ax.set_xlabel(u'm')\n",
    "\n",
    "plt.xlabel('X [m]')\n",
    "plt.xlim(160, 260)\n",
    "plt.ylabel('Y [m]')\n",
    "plt.ylim(110, 160)\n",
    "plt.title('Position')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, complicated analytic calculation of the Jacobian Matrices, but it works pretty well."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write Google Earth KML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert back from Meters to Lat/Lon (WGS84)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "latekf = latitude[0] + np.divide(x1,arc)\n",
    "lonekf = longitude[0]+ np.divide(x0,np.multiply(arc,np.cos(latitude*np.pi/180.0)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Data for KML Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Coordinates and timestamps to be used to locate the car model in time and space\n",
    "The value can be expressed as yyyy-mm-ddThh:mm:sszzzzzz, where T is the separator between the date and the time, and the time zone is either Z (for UTC) or zzzzzz, which represents ±hh:mm in relation to UTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "car={}\n",
    "car['when']=[]\n",
    "car['coord']=[]\n",
    "car['gps']=[]\n",
    "for i in range(len(millis)):\n",
    "    d=datetime.datetime.fromtimestamp(millis[i]/1000.0)\n",
    "    car[\"when\"].append(d.strftime(\"%Y-%m-%dT%H:%M:%SZ\"))\n",
    "    car[\"coord\"].append((lonekf[i], latekf[i], 0))\n",
    "    car[\"gps\"].append((longitude[i], latitude[i], 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simplekml import Kml, Model, AltitudeMode, Orientation, Scale, Style, Color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exported KMZ File for Google Earth\n"
     ]
    }
   ],
   "source": [
    "# The model path and scale variables\n",
    "car_dae = r'https://raw.githubusercontent.com/balzer82/Kalman/master/car-model.dae'\n",
    "car_scale = 1.0\n",
    "\n",
    "# Create the KML document\n",
    "kml = Kml(name=d.strftime(\"%Y-%m-%d %H:%M\"), open=1)\n",
    "\n",
    "# Create the model\n",
    "model_car = Model(altitudemode=AltitudeMode.clamptoground,\n",
    "                            orientation=Orientation(heading=75.0),\n",
    "                            scale=Scale(x=car_scale, y=car_scale, z=car_scale))\n",
    "\n",
    "# Create the track\n",
    "trk = kml.newgxtrack(name=\"EKF\", altitudemode=AltitudeMode.clamptoground,\n",
    "                     description=\"State Estimation from Extended Kalman Filter with CTRA Model\")\n",
    "\n",
    "# Attach the model to the track\n",
    "trk.model = model_car\n",
    "trk.model.link.href = car_dae\n",
    "\n",
    "# Add all the information to the track\n",
    "trk.newwhen(car[\"when\"])\n",
    "trk.newgxcoord(car[\"coord\"])\n",
    "\n",
    "# Style of the Track\n",
    "trk.iconstyle.icon.href = \"\"\n",
    "trk.labelstyle.scale = 1\n",
    "trk.linestyle.width = 4\n",
    "trk.linestyle.color = '7fff0000'\n",
    "\n",
    "# Add GPS measurement marker\n",
    "fol = kml.newfolder(name=\"GPS Measurements\")\n",
    "sharedstyle = Style()\n",
    "sharedstyle.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/shapes/placemark_circle.png'\n",
    "\n",
    "for m in range(len(latitude)):\n",
    "    if GPS[m]:\n",
    "        pnt = fol.newpoint(coords = [(longitude[m],latitude[m])])\n",
    "        pnt.style = sharedstyle\n",
    "\n",
    "# Saving\n",
    "kml.savekmz(\"Extended-Kalman-Filter-CTRA.kmz\")\n",
    "print('Exported KMZ File for Google Earth')"
   ]
  }
 ],
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