Nonparametric rank detectors under K-distributed clutter in radar applications

Sanz-González, J.L.; Álvarez-Vaquero, F.

doi:10.1109/taes.2005.1468759

Cited by 15 publications

(17 citation statements)

References 19 publications

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“…Similar tendencies are observed for the SWII case when it is compared with the NF case, although the SCR required in SWII case is greater than the SCR required in the NF case for the same P d . Other results have been published in [42,43].…”

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

confidence: 83%

“…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: 86%

“…Additional results can be found in [42,43]. The clutter shape parameter (v) has been v=0.5 as a typical value for spiky clutter, and v = co for Rayleigh clutter.…”

Section: Detector Performance Analysismentioning

confidence: 99%

“…The spherically invariant random process (SIRP), as clutter model for the range cell under test and a block of N pulses [43], can be represented by the compound model: X = W • Y, where X and W are N-dimensional complex random column vectors, and Y is a real random scalar (independent of W). Also, W = (VV!,VV 2 ,...,VVJV) T is a complex-Gaussian random column vector, and the components W i} i=\, 2 N, are mutually independent if the radar system uses frequency agility from pulse-to-pulse.…”

Section: Sirp Clutter Modelmentioning

confidence: 99%

“…2 for easy comparisons. Other results have been published in [34,43]. We use the following symbols: continuous lines for denoting SIRP (K-distributed) clutter with v=0.5, discontinuous (dotted) lines for denoting Rayleigh clutter; lines with " + " for the optimum parametric detector, lines with "<>" for the optimum permutation detector, and lines with "o" for the optimum rank detector.…”

Section: Analysis Under Sirp (K-distributed) Cluttermentioning

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: 83%

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

confidence: 86%

“…Additional results can be found in [42,43]. The clutter shape parameter (v) has been v=0.5 as a typical value for spiky clutter, and v = co for Rayleigh clutter.…”

Section: Detector Performance Analysismentioning

confidence: 99%

Section: Sirp Clutter Modelmentioning

confidence: 99%

Section: Analysis Under Sirp (K-distributed) Cluttermentioning

confidence: 99%

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Permutation tests for nonparametric detection

2012

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A Monte Carlo simulation for two novel automatic censoring techniques of radar interfering targets in log-normal clutter

2008

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Track‐before‐detect algorithm based on dynamic programming for multi‐extended‐targets detection

Yan

et al. 2017

IET signal process.

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In recent years, multi-extended-targets detection in sea clutter has gained a special interest. Dynamic programming based track-before-detect (DP-TBD) algorithm is used to detect extended targets in video data of high resolution radars. Two innovations are presented in this work. First one is a novel partition method to cluster targets into well separate groups for the problem of high-dimensional maximisation. Second one is a novel merit function specifically designed for extended targets for the problem of target extended. Both the principle of this novel DP-TBD method specifically for extended targets and its detail implementation are presented. Then, both the real data and simulated data are performed. The comparison of the results shows that compared with particle filter based track before detect algorithm, the proposed method obtains better performance in detection rate, position error and false alarm rate. Meanwhile, far less calculation is spent with the novel partition method. Therefore, it can safely conclude that the proposed method is very practical under various sea conditions in the real world.

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Nonparametric rank detectors under K-distributed clutter in radar applications

Cited by 15 publications

References 19 publications

Permutation tests for nonparametric detection

Permutation tests for nonparametric detection

A Monte Carlo simulation for two novel automatic censoring techniques of radar interfering targets in log-normal clutter

Track‐before‐detect algorithm based on dynamic programming for multi‐extended‐targets detection

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