A Suboptimum Rank Test for Nonparametric Radar Detection

Sanz-González, J.L.; Figueros-Vidal, A.R.

doi:10.1109/taes.1986.310800

Cited by 8 publications

(9 citation statements)

References 19 publications

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“…Other way of computing ranks is as follows [22,23] n r = (r!,r 2 ,...,r"); r t = ^ u(x;-x,<), i=l,2,...,n k = l 1 <fj <n.…”

Section: Relationship Between Permutation Tests and Rank Testsmentioning

confidence: 99%

“…In a first analysis, the two hypotheses H 0 (target absent) and Hi (target present) are defined in terms of probability density functions, which are as follows for nonfluctuating or Swerling II target models [22,24] …”

Section: Iid Clutter Modelmentioning

confidence: 99%

“…In this section, we present a sample of the results of P d versus signal-to-clutter ratio (SCR) for some optimum nonparametric permutation (and rank [22,24]) tests (detectors) against optimum parametric detectors [32] under K-distributed clutter and nonfluctuating (NF) and Swerling II (SWII) target models. Additional results can be found in [42,43].…”

Section: Detector Performance Analysismentioning

confidence: 99%

“…Interesting classical tutorial papers about nonparametric detection are due to Carlyle and Thomas [18] and Thomas [19]. Applications of rank tests to radar detection can be found in [20][21][22][23][24] and references therein. Other rank applications to (wireless) communications are, for example, in [25][26][27][28][29] and references therein.…”

Section: Introductionmentioning

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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“…Other way of computing ranks is as follows [22,23] n r = (r!,r 2 ,...,r"); r t = ^ u(x;-x,<), i=l,2,...,n k = l 1 <fj <n.…”

Section: Relationship Between Permutation Tests and Rank Testsmentioning

confidence: 99%

Section: Iid Clutter Modelmentioning

confidence: 99%

Section: Detector Performance Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Permutation tests for nonparametric detection

2012

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Geometric means and medians with applications to target detection

Hua

Cheng

Wang

et al. 2017

IET signal process.

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This study explores the application of geometric measures‐based means and medians on the Riemannian manifold of Hermitian positive‐definite (HPD) matrix to target detection problems in radar systems. Firstly, the slow‐time dimension of radar received clutter data in each cell is modelled and mapped to HPD matrix space, which can be described as a complex Riemannian manifold. Each point of this manifold is an HPD matrix. Then, several geometric measures are presented for measuring closeness between two HPD matrices. According to these measures, the means and medians of a finite collection of HPD matrices are deduced, and various matrix constant false alarm rate (CFAR) detectors are designed. The principle of target detection is that if a location has enough dissimilarity from the geometric mean or median estimated by its neighbouring locations, targets are supposed to appear at this location. Moreover, different distance measures adopted in detector can result in different performance of detection. These differences are owing to different measure structure, reflected by the anisotropy of a location on the Riemannian manifold. Numerical experiments are given to demonstrate the relationship between anisotropy of the geometric measures and the detection performance of their corresponding matrix CFAR detectors.

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Nonparametric rank detectors on quantized radar video signals

Sanz-González

1990

IEEE Trans. Aerosp. Electron. Syst.

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A Suboptimum Rank Test for Nonparametric Radar Detection

Cited by 8 publications

References 19 publications

Permutation tests for nonparametric detection

Permutation tests for nonparametric detection

Geometric means and medians with applications to target detection

Nonparametric rank detectors on quantized radar video signals

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