Nonparametric permutation tests versus parametric tests in radar detection under k-distributed clutter

Gonzalez-Garcia, Jose E.; Sanz-González, J.L.; Alvarez-Vaquerod, F.

doi:10.1109/radar.2005.1435829

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…From results not shown here (see [32,34,42,43]), for spiky clutter (v=0.5), the degradation in SCR of each detector when P fa changes from 10~3 to 10~6 is about 5dB, and from P /a =10~6 to 10~8, the degradation is about 2 dB. On the other hand, for Rayleigh clutter, there are SCR-degradations of 2 dB approximately from P/ a =10~3 to 10~6, and 1 dB approximately from P/ a =10~6 to 10~8.…”

Section: Analysis Under Iid (K-distributed) Clutter Samplesmentioning

confidence: 88%

“…According to our previous works on parametric and nonparametric (rank and permutation) detectors [22][23][24]32] under IID clutter samples, the optimum statistic for parametric and permutation detectors is the clipping statistic [32] under spiky K-distributed clutter (v=0.5) and the linear (or quadratic) statistic under Rayleigh clutter (v = co). For rank detectors [22,24], the linear rank statistic is quasi-optimum under spiky clutter (v=0.5) and suboptimum under Rayleigh clutter (v = co).…”

Section: Analysis Under Iid (K-distributed) Clutter Samplesmentioning

confidence: 99%

“…However, the converse is not true, because T(X)=/ X (X|H!) is also an optimum permutation test statistic according to (32), but it is not an optimum statistic for the Neyman-Pearson test. Furthermore, we also conclude that T(X)=/ X (X|H,) and l(x)=(/ x (x|H 1 )// x (x|H 0 )) are equivalent permutation test statistics, because both generate the same permutation partition.…”

Section: Appendix I Proof Of the Main Theoremmentioning

confidence: 99%

“…Other rank applications to (wireless) communications are, for example, in [25][26][27][28][29] and references therein. Finally, although rank tests have been applied to detection [16][17][18][19][20][21][22][23][24][25][26][27][28][29], the more general family of permutation tests has seldom been applied to radar detection [30][31][32][33][34][35], at least, as far as the authors know. Now, some comments on the methodology of test optimization are required.…”

Section: Introductionmentioning

confidence: 99%

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Permutation tests for nonparametric detection

2012

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In this paper, the authors provide a methodology to design nonparametric permutation tests and, in particular, nonparametric rank tests for applications in detection. In the first part of the paper, the authors develop the optimization theory of both permutation and rank tests in the Neyman-Pearson sense; in the second part of the paper, they carry out a comparative performance analysis of the permutation and rank tests (detectors) against the parametric ones in radar applications.First, a brief review of some contributions on nonparametric tests is realized. Then, the optimum permutation and rank tests are derived. Finally, a performance analysis is realized by Monte-Carlo simulations for the corresponding detectors, and the results are shown in curves of detection probability versus signal-to-noise ratio.

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Section: Analysis Under Iid (K-distributed) Clutter Samplesmentioning

confidence: 88%

Section: Analysis Under Iid (K-distributed) Clutter Samplesmentioning

confidence: 99%

Section: Appendix I Proof Of the Main Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Permutation tests for nonparametric detection

2012

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“…Some examples of nonparametric radar detectors can be found in [1][2][3]. In the past, some nonparametric detectors have been applied to radar detection [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

New nonparametric detectors under K-distributed sea clutter in radar applications

Zhao

Ruilai

Huang

et al. 2011

Proceedings of 2011 IEEE CIE International Conference on Radar

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Based on modified rank test, this paper proposes two new nonparametric detectors by using the K-distributed sea clutter model. These two detectors are Modified-Rank-based Generalized Sign detector (MRGS) and Modified-Rank-based Mann-Whitney detector (MRMW). The new detectors are achieved by designing the inverse-normal-score functions of the rank statistics. They are CFAR (Constant False Alarm Rate) for the data with continuous CDF (Cumulative Distribution Function) and with PDF (Probability Density Function) bing symmetric about zero. The performance of the new detectors is analyzed under the Swerling II target model by Monte-Carlo simulation.We compare the results of our proposed detectors with two classical nonparametric detectors: Generalized Sign (GS) detector and Mann-Whitney (MW) Detector. The most important conclusion is that the detectability of nonparametric detector is insensitive to the change of the scale parameter b but just depends on the shape parameter Ȟ of the K-distributed clutter for the Swerling II target model. Moreover, nonparametric detector especially for the proposed MRMW detector in this paper is quite suitable for low observable targets detection under spiky Kdistributed sea clutter.

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Design and analysis of fractal detector for high resolution radars

Salmasi

Modarres-Hashemi

2009

Chaos, Solitons & Fractals

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Nonparametric permutation tests versus parametric tests in radar detection under k-distributed clutter

Cited by 7 publications

References 10 publications

Permutation tests for nonparametric detection

Permutation tests for nonparametric detection

New nonparametric detectors under K-distributed sea clutter in radar applications

Design and analysis of fractal detector for high resolution radars

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