多路径对n阶时间反转分类器的影响

F. Foroozan, A. Asif
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引用次数: 1

摘要

本文推导了n元分类问题的时间反转算法,并研究了多路径对算法性能特性的影响。TR分类器具有在具有多路径的丰富杂波环境中检测目标存在或不存在的能力,并进一步将检测到的目标分类为未知敌方目标或已知友军目标之一。与传统分类器相比,所提出的TR分类器在低信噪比下提供了超过5dB的增益。采用基于几何光学近似的解析多径模型,结合介质边界的强全内反射,比较了多径对分类器和传统算法的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Effect of Multipaths on N-Ary Time Reversal Classifiers
The paper derives time reversal (TR) algorithms for N-ary classification problems and studies the impact of multipaths on the performance characteristics of these algorithms. The TR classifiers have the ability of detecting the presence or absence of a target in a rich cluttering environment with multipaths and of further classifying the detected targets either as an unknown enemy target or one of the known friendly targets. Compared to the conventional classifiers, the proposed TR classifier provides a gain of over 5dB at low SNRs. An analytical multipath model, based on geometric optics approximation with strong total internal reflection from the boundaries of the medium, is used to compare the effect of multipaths on the proposed classifiers versus the conventional algorithms.
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