A canonical representation for distributions of adaptive matched subspace detectors

Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136) Pub Date : 1997-11-02 DOI:10.1109/ACSSC.1997.679120

S. Kraut, L. T. McWhorter, L. Scharf

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引用次数: 20

Abstract

We present a unified derivation of the distributions for adaptive versions of matched subspace detectors (MSDs) derived by Scharf (see Statistical Signal Processing, Addison-Wesley, and IEEE Trans. Signal Processing, 1996). These include: (1) the matched filter detector, (2) the gain invariant (CFAR) matched filter detector (3) the phase invariant matched subspace detector, and (4) the gain invariant (CFAR) and phase invariant matched subspace detector. We show that all these detectors can be decomposed into representations that are simple functions of the same five statistically independent, chi-squared or normal, scalar random variables. This canonical representation has at least three advantages: (1) the behavior of these detectors can easily be related to that of the non-adaptive detectors from which they are derived (2) moments can be simply obtained from the distributions of the scalar random variables, and (3) Monte Carlo simulations of the distributions can be implemented more efficiently.

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自适应匹配子空间检测器分布的规范表示

我们提出了由Scharf导出的匹配子空间检测器(MSDs)的自适应版本的分布的统一推导(参见统计信号处理，Addison-Wesley和IEEE Trans)。信号处理，1996)。这些包括:(1)匹配滤波器检测器，(2)增益不变(CFAR)匹配滤波器检测器，(3)相位不变匹配子空间检测器，以及(4)增益不变(CFAR)和相位不变匹配子空间检测器。我们表明，所有这些检测器都可以分解为表示，这些表示是相同的五个统计独立的卡方或正态标量随机变量的简单函数。这种规范表示至少有三个优点:(1)这些检测器的行为可以很容易地与推导它们的非自适应检测器的行为相关联;(2)矩可以简单地从标量随机变量的分布中获得;(3)分布的蒙特卡罗模拟可以更有效地实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136)

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