Target classification for phase array radar using the minimum norm criteria

A. J. Willis, R. de Mello Koch
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Abstract

Using SVD a reconstruction consistent with the minimum norm criteria is derived. The resulting algorithm is able to profile an arbitrary target bearing distribution from the readings received at an arbitrary number of sensors with arbitrary directional characteristics and locations; in a noisy environment. The algorithm produced using this method represents an advance on previous multiple snapshot techniques designed to identify a limited number of point targets through parametric search. The more rapid process of a single matrix decomposition is employed to give the minimum norm estimate applicable to an arbitrary target profile-this being derived in principle from a single snapshot. The algorithm is well suited to the inclusion of a priori knowledge, which gives flexibility in speed and resolution.<>
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基于最小范数准则的相控阵雷达目标分类
利用奇异值分解得到了符合最小范数准则的重构。所得到的算法能够从具有任意方向特征和位置的任意数量的传感器接收的读数中勾画出任意目标方位分布;在嘈杂的环境中。使用该方法产生的算法代表了先前的多快照技术的进步,这些技术旨在通过参数搜索识别有限数量的点目标。采用更快速的单矩阵分解过程来给出适用于任意目标轮廓的最小范数估计-这原则上是从单个快照导出的。该算法非常适合包含先验知识,从而在速度和分辨率上具有灵活性
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