Detection Performance of the Cell Averaging LOG/CFAR Receiver

Hansen, V. Gregers; Ward, H.R.

doi:10.1109/taes.1972.309580

Cited by 53 publications

(20 citation statements)

References 2 publications

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“…40 log-CFAR introduces an additional CFAR loss into the process. To circumvent this CFAR loss, the size of the CFAR stencil, more precisely the boundary ring in the stencil, needs to be increased by 65 percent 58 by following…”

Section: Cfar-based Methodsmentioning

confidence: 99%

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

El-Darymli

McGuire

Power

et al. 2013

J. Appl. Remote Sens

178

112

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Abstract. Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidean distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Cfar-based Methodsmentioning

confidence: 99%

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

El-Darymli

McGuire

Power

et al. 2013

J. Appl. Remote Sens

178

112

View full text Add to dashboard Cite

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“…The additional process normalizes the echo intensity to compensate for the spatial variation of the mean echo intensity. Because CA CFAR detectors were designed to detect targets while maintaining a constant probability of false alarm [22,23], they were used for target detection with high-intensity echoes in an inhomogeneous medium [24,25]. However, the echo intensity from a small calcification is low, and thus small calcification detection is difficult when only selecting high-intensity echo masses.…”

Section: Introductionmentioning

confidence: 99%

Small calcification indicator in ultrasonography using correlation of echoes with a modified Wiener filter

et al. 2012

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“…For extraction of high-echo-intensity regions, many researchers have employed a constant false alarm rate (CFAR) technique [5,6]. A CFAR detector extracts targets in nonstationary noise and clutter while maintaining a constant probability of a false alarm [7,8]. Therefore, a small calcification with low echo intensity is almost never detected using CFAR detectors.…”

Section: Introductionmentioning

confidence: 99%