A low-complexity radar detector outperforming OS-CFAR for indoor drone obstacle avoidance

Ali Safa*, Tim Verbelen, Lars Keuninckx, Ilja Ocket, Mathias Hartmann, Andre Bourdoux, Francky Catthoor, Georges G.E. Gielen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)9162-9175
Number of pages14
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume14
DOIs
Publication statusPublished - 2021
Externally publishedYes

Keywords

  • Constant false alarm rate (CFAR)
  • drone navigation
  • indoor drone obstacle avoidance
  • indoor radar sensing
  • radar target detection

Fingerprint

Dive into the research topics of 'A low-complexity radar detector outperforming OS-CFAR for indoor drone obstacle avoidance'. Together they form a unique fingerprint.

Cite this