This repository contains an example code comparing our proposed detector to OS-CFAR, a video showcasing the reduced computational complexity of our system compared to standard detection schemes and a video showing our proposed detector working on-line on a drone.
If you use this work, please cite:
A. Safa et al., "A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2021.3107686.
Please check-out the example code and dataset provided in this repository. It provides a Python implementation of our proposed detector and of OS-CFAR. By running the code, the Receiver Operating Curves (ROC) of our proposed detector and OS-CFAR showing the probability of detection vs. the probability of false alarm are computed and shown.
Compared to standard 2D CA-CFAR detection, our proposed detector significantly reduces the detection complexity, enabling higher detection frames.
hardware_complexity_vid.mp4
We implemented our detector in the ARM Cortex R4 MCU of the AWR1443 radar used throughout this work. The video below shows our detector working in real-time on-line at 30 FPS. The structure of the room (shelves, wall,...) can be easily seen in the radar detections which qualitatively shows the effectiveness of our proposed method.