TL;DR
This paper introduces a novel low-complexity radar detection algorithm that significantly outperforms traditional OS-CFAR in indoor drone obstacle avoidance scenarios, enhancing detection probability amidst dense clutter.
Contribution
The authors propose a new high-performance, low-complexity radar detector inspired by non-linear target detection, validated with real drone-mounted radar data, outperforming existing CFAR methods.
Findings
Over 19% higher detection probability than OS-CFAR.
16% improvement over CHA-CFAR in indoor scenarios.
Significant enhancement over existing CFAR detectors in dense clutter environments.
Abstract
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios, radars must perform well in dense scenes with a large number of neighboring scatterers. Central to radar performance is the detection algorithm used to separate targets from the background noise and clutter. Traditionally, most radar systems use conventional CFAR detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in non-linear target detection, we propose a novel high-performance, yet low-complexity target detector and we experimentally validate our algorithm on a dataset acquired using a radar mounted on a drone. We experimentally show that our proposed algorithm drastically outperforms OS-CFAR (standard detector used in automotive systems) for our…
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