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Proceedings Paper

Signal processing algorithms by permutation test in radar application
Author(s): Francisco Alvarez-Vaquero; Jose L. Sanz-Gonzalez
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Paper Abstract

A hypothesis H is parametric if every distribution from the process defined by H belongs to a family of distributions characterized by a finite number of parameters; on the other hand, if the distribution can not be defined by a finite number of parameters, the hypothesis is nonparametric. In this paper, we analyze a detector based on the optimum permutation test, applied to nonparametric radar detection which provide good performances without a large computational work, and we compare it with the parametric test and rank test in the Neyman-Pearson sense. The computational complexity of the detector is high and its implementation in real time is difficult, due to the number of operations increase with the factorial of the number of samples. Also, we present an algorithm that reduces the computational work required. We also present the detectability characteristic of the optimum permutation test against rank test and parametric test under Gaussian noise environments and different types of target models (nonfluctuating, Swerling I and Swerling II). The detection probability versus signal-to-noise ratio is estimated by Monte-Carlo simulations for different parameter values (N pulse, M reference samples and false alarm probability Pfa).

Paper Details

Date Published: 22 October 1996
PDF: 7 pages
Proc. SPIE 2846, Advanced Signal Processing Algorithms, Architectures, and Implementations VI, (22 October 1996); doi: 10.1117/12.255427
Show Author Affiliations
Francisco Alvarez-Vaquero, Univ. Politecnica de Madrid (Spain)
Jose L. Sanz-Gonzalez, Univ. Politecnica de Madrid (Spain)


Published in SPIE Proceedings Vol. 2846:
Advanced Signal Processing Algorithms, Architectures, and Implementations VI
Franklin T. Luk, Editor(s)

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