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 constant false alarm rate (CFAR) detectors but their performance degrades in indoor scenarios with many reflectors. Inspired by the advances in nonlinear target detection, In this article, 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 ordered statistics CFAR (OS-CFAR) (standard detector used in automotive systems) for our specific task of indoor drone navigation with more than 19% higher probability of detection for a given probability of false alarm. We also benchmark our proposed detector against a number of recently proposed multitarget CFAR detectors and show an improvement of 16% in probability of detection compared to censored harmonic averaging CFAR, with even larger improvements compared to both outlier-robust CFAR and truncated statistics log-normal CFAR in our particular indoor scenario. To the best of authors’ knowledge, this article improves the state-of-the-art for high-performance yet low-complexity radar detection in critical indoor sensing applications.
Highlights
T HE use of radar sensors for indoor applications has been enabled recently thanks to the enormous progress in radar miniaturization [1], [2] and energy efficiency [3], making use Manuscript received May 27, 2021; revised July 12, 2021; accepted August 22, 2021
The complexity of our proposed algorithm is O(LD) compared to O(LN2tc) and O(LNtc ln (Ntc)) for ordered statistics (OS)-based methods and O(L) for cell averaging (CA)-based methods [32], where D is rather small as (21) projects scalars to vectors
It is visually clear that our method significantly outperforms OS-constant false alarm rate (CFAR) without the requirement for computationally expensive sorting operations per cell under test (CUT)
Summary
T HE use of radar sensors for indoor applications has been enabled recently thanks to the enormous progress in radar miniaturization [1], [2] and energy efficiency [3], making use Manuscript received May 27, 2021; revised July 12, 2021; accepted August 22, 2021. We have observed that OS-CFAR fails to detect all potential targets systematically when processing radar signals during an indoor drone flight (a discussion is provided in Section V-D), while achieving a PD close to 1 is essential for a safe obstacle avoidance. SAFA et al.: LOW-COMPLEXITY RADAR DETECTOR OUTPERFORMING OS-CFAR FOR INDOOR DRONE OBSTACLE AVOIDANCE yet low-complexity detection algorithm featuring a higher PD for a certain PFA compared to the conventional CFARs, including OS-CFAR and more recent, state-of-the-art CFAR variants such as outlier-robust CFAR (OR-CFAR) [40], truncated statistics (TS) log-normal (CFAR) TS-LNCFAR [41], and censored harmonic averaging CFAR (CHA-CFAR) [42].
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