Detectability Loss Due to "Greatest Of" Selection in a Cell-Averaging CFAR

Hansen, V. Gregers; Sawyers, James H.

doi:10.1109/taes.1980.308885

Cited by 199 publications

(70 citation statements)

References 2 publications

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“…In this detector, the adaptive threshold is the arithmetic mean of its reference cells. Many CFAR algorithms are developed to meet different situations namely in a transition in the clutter power distribution [12,13] and the presence of interfering targets [5,6,8,14,15,21,22,24] which refer to targets present in the reference window cells. When the number of interfering targets exceeds the supposed one, the developed detectors exhibit a serious performance degradation.…”

Section: Problem Formulationmentioning

confidence: 99%

Automatic Threshold Selection in Os-Cfar Radar Detection Using Information Theoretic Criteria

Magaz¹,

Belouchrani²,

Hamadouche³

2011

PIER B

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Abstract-This paper proposes a new approach for efficiently determining the unwanted interfering samples in the reference window, for the ordered statistics constant false alarm rate detector, based on the application of the information theoretic criteria principle. The proposed processor termed as Forward Automatic Order Selection Ordered Statistics Detector (FAOSOSD) does not require any prior information about the number of interfering targets. The proposed design aims to improve the Ordered Statistics Constant False Alarm Rate detector performance under severe interference situations. The number of interfering targets is obtained by minimizing the information theoretic criteria. Simulation results that illustrate the performance of the proposed method versus the classical OS-CFAR, the AND-CFAR and the OR-CFAR detectors are presented and discussed.

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Section: Problem Formulationmentioning

confidence: 99%

Automatic Threshold Selection in Os-Cfar Radar Detection Using Information Theoretic Criteria

Magaz¹,

Belouchrani²,

Hamadouche³

2011

PIER B

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“…Il=P.S (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) where P = 2 + 2 and has Rayleigh distribution, and S has the generalized Chi distribution. Let's denote the product PS by W, then the density function of Kdistributed random variable W is ,lfLw)=-2 ._ 2 K,,-,(cw) , w >() (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) where a is the shape parameter, c is the scale parameter and Kv(x) is the modified Bessel function of the second kind.…”

Section: Simulation Proceduresmentioning

confidence: 99%

“…So In simulation of detection performance, the clutter samples in the reference window are generated from I 2 and the test cell sample from 101". (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) Then the signal to clutter power ratio, SCR, is given by…”

Section: Simulation Proceduresmentioning

confidence: 99%

Intelligent Use of CFAR Algorithms

Antonik¹,

Bowles²,

Capraro³

et al. 1993

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“…Among them, the constant false alarm rate (CFAR) detectors are mostly widely used. For example, the cell-averaged CFAR (CA-CFAR) and the order statistic CFAR (OS-CFAR) [3][4][5][6] are two classic one-parameter CFAR detectors; they are both considered to adaptively determine the detection threshold by using the statistics estimated from the boundaries of the detecting window. The background clutters are usually modeled by a Gaussian distribution.…”

Section: Introductionmentioning

confidence: 99%

Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision

et al. 2015

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The robust detection of ships is one of the key techniques in coastal and marine applications of synthetic aperture radar (SAR). Conventional SAR ship detectors involved multiple parameters, which need to be estimated or determined very carefully. In this paper, we propose a new ship detection approach based on multi-scale heterogeneities under the a contrario decision framework, with a few parameters that can be easily determined. First, multi-scale heterogeneity features are extracted and fused to build a heterogeneity map, in which ships are well highlighted from backgrounds. Second, a set of reference objects are automatically selected by analyzing the saliency of local regions in the heterogeneity map and then are used to construct a null hypothesis model for the final decision. Finally, the detection results are obtained by using an a contrario decision. Experimental results on real SAR images demonstrate that the proposed method not only works more stably for ships with different sizes, but also has better performance than conventional ship detectors.

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Detectability Loss Due to "Greatest Of" Selection in a Cell-Averaging CFAR

Cited by 199 publications

References 2 publications

Automatic Threshold Selection in Os-Cfar Radar Detection Using Information Theoretic Criteria

Automatic Threshold Selection in Os-Cfar Radar Detection Using Information Theoretic Criteria

Intelligent Use of CFAR Algorithms

Automatic Ship Detection in SAR Images Using Multi-Scale Heterogeneities and an A Contrario Decision

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