2021/07/15 by Ali Safa, Tim Verbelen, Safa, Ali +12 · 1 citation
Engineering · #Advanced SAR Imaging Techniques #FOS: Computer and information sciences #FOS: Electrical engineering #Microwave Imaging and Scattering Analysis #Radar Systems and Signal Processing #Robotics (cs.RO) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2107.07250
openalex publication_date 2021/07/15 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
As radar sensors are being miniaturized, there is a growing interest for\nusing them in indoor sensing applications such as indoor drone obstacle\navoidance. In those novel scenarios, radars must perform well in dense scenes\nwith a large number of neighboring scatterers. Central to radar performance is\nthe detection algorithm used to separate targets from the background noise and\nclutter. Traditionally, most radar systems use conventional CFAR detectors but\ntheir performance degrades in indoor scenarios with many reflectors. Inspired\nby the advances in non-linear target detection, we propose a novel\nhigh-performance, yet low-complexity target detector and we experimentally\nvalidate our algorithm on a dataset acquired using a radar mounted on a drone.\nWe experimentally show that our proposed algorithm drastically outperforms\nOS-CFAR (standard detector used in automotive systems) for our specific task of\nindoor drone navigation with more than 19% higher probability of detection for\na given probability of false alarm. We also benchmark our proposed detector\nagainst a number of recently proposed multi-target CFAR detectors and show an\nimprovement of 16% in probability of detection compared to CHA-CFAR, with even\nlarger improvements compared to both OR-CFAR and TS-LNCFAR in our particular\nindoor scenario. To the best of our knowledge, this work improves the state of\nthe art for high-performance yet low-complexity radar detection in critical\nindoor sensing applications.\n