Robust CFAR Detector Based on Censored Harmonic Averaging in Heterogeneous Clutter

Zefreh, Reza Ghaderi; Taban, Mohammad Reza; Naghsh, Mohammad Mahdi; Gazor, Saeed

doi:10.1109/taes.2020.3046050

Cited by 15 publications

(13 citation statements)

References 36 publications

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“…Recently, a number of CFAR detectors based on the truncated statistics principle have been proposed for multi-target detection in outdoor environments (e.g., ship detection from SAR images), using a careful modelling of the clutter statistics [40]- [42]. In addition to OS-CFAR, the applicability of those aforementioned methods to indoor scenarios is also investigated in this work.…”

Section: Related Workmentioning

confidence: 99%

“…3) Censored harmonic averaging CFAR (CHA-CFAR): CHA-CFAR [42] has been proposed as a well-suited detector for exponential clutter scenarios affected by a large number of outliers. In contrast to methods that seek to hardly remove outliers by truncation (such as in OR-CFAR and TS-LNCFAR), CHA-CFAR softly removes the effect of outliers using the harmonic mean and the OS principle to estimate the noise level ω:…”

Section: State-of-the-art Cfar Principles For Multi-target Scenariosmentioning

confidence: 99%

“…where {z 1 , ...z N } is a set obtained by sorting the elements of Z[x] in increasing order and m is the number of discarded samples (with the smallest amplitude) [42]. A CUT of amplitude S is reported as a detection if S > tω where t is a parameter setting the desired P F A .…”

Section: State-of-the-art Cfar Principles For Multi-target Scenariosmentioning

confidence: 99%

“…Thus, reliable radar target detection remains a challenge as missed detections can jeopardize the obstacle avoidance mechanisms and may crash the drone. Therefore, there is a need for a better, yet low-complexity detection algorithm featuring a higher P D for a certain P F A compared to the conventional CFARs, not only including OS-CFAR but also more recent, state-of-the-art CFAR variants such as OR-CFAR [40], TS-LNCFAR [41] and CHA-CFAR [42].…”

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confidence: 99%

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A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance

Safa¹,

Verbelen²,

Keuninckx³

et al. 2021

Preprint

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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 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 multi-target CFAR detectors and show an improvement of 16% in probability of detection compared to CHA-CFAR, with even larger improvements compared to both OR-CFAR and TS-LNCFAR in our particular indoor scenario. To the best of our knowledge, this work improves the state of the art for high-performance yet low-complexity radar detection in critical indoor sensing applications.

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Section: Related Workmentioning

confidence: 99%

Section: State-of-the-art Cfar Principles For Multi-target Scenariosmentioning

confidence: 99%

Section: State-of-the-art Cfar Principles For Multi-target Scenariosmentioning

confidence: 99%

mentioning

confidence: 99%

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A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance

Safa¹,

Verbelen²,

Keuninckx³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The performance of CFAR detection depends on the design of sliding window, statistical modeling of clutter distribution, and estimation of model parameters [8]. Subsequent works mainly focus on these aspects to design effective detection algorithms [9]. Over the years, many window structures and statistical models have been designed in the CFAR framework to deal with clutter and complex background distributions [10].…”

Section: Introductionmentioning

confidence: 99%