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Design and analysis of a knowledge-aided radar detector for doppler processing

机译：一种用于多普勒处理的知识辅助雷达探测器的设计与分析

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摘要

In this paper we discuss the combined use of a priori information and adaptive signal processing techniques for the design and the analysis of a knowledge-aided (KA) radar receiver for Doppler processing. To this end, resorting to the generalized likelihood function (GLF) criterion (both one-step and two-step), we design and assess data-adaptive procedures for the selection of training data. Then we introduce a KA radar detector composed of three elements: a geographic-map-based data selector, which exploits some a priori information concerning the topography of the observed scene, a data-adaptive training selector which removes dynamic outliers from the training data, and an adaptive radar detector which performs the final decision about the target presence. The performance of the KA algorithm is analyzed both on simulated as well as on real radar data collected by the McMaster University IPIX radar. The results show that the new KA system achieves a satisfactory performance level and can outperform some previously proposed adaptive detection schemes
机译：在本文中，我们将讨论先验信息和自适应信号处理技术的结合使用，以设计和分析用于多普勒处理的知识辅助（KA）雷达接收机。为此，我们采用广义似然函数（GLF）准则（一步骤和两步骤），设计和评估用于选择训练数据的数据自适应程序。然后，我们介绍了一个由三个元素组成的KA雷达探测器：一个基于地理地图的数据选择器，它利用了有关所观察场景地形的一些先验信息；一个数据自适应训练选择器，从训练数据中去除了动态离群值；自适应雷达探测器对目标的存在做出最终决定。在麦克马斯特大学IPIX雷达收集的模拟以及真实雷达数据上都分析了KA算法的性能。结果表明，新的KA系统达到了令人满意的性能水平，并且可以优于以前提出的一些自适应检测方案

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