Secondary data support in space-time adaptive processing

Proceedings of the 1998 IEEE Radar Conference, RADARCON'98. Challenges in Radar Systems and Solutions (Cat. No.98CH36197) Pub Date : 1998-05-11 DOI:10.1109/NRC.1998.677998

T. Hale, B. Welsh

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引用次数: 3

Abstract

One of the primary problems with the application of space-time adaptive processing (STAP) techniques to radar is secondary data support for the interference plus noise covariance matrix estimate. Reed (1974) has shown the required secondary data support to achieve performance within 3 dB of optimal SINR is approximately twice the degrees of freedom (DOF). Reed proved this rule for sample matrix inversion (SMI) techniques. A concern arises when applying this rule to a newer class of reduced dimension STAP algorithms that do not fall under the SMI umbrella. This paper focuses on the cross spectral metric (CSM) algorithm (Goldstein and Reed, 1997). Through Monte Carlo simulations, Reed's rule for sample support is examined for this non-SMI technique. Optimum SINR performance for the CSM algorithm is obtained by choosing the number of DOF in the algorithm equal to the interference subspace dimension. With this choice, the required sample support for the covariance matrix estimate is approximately 2.5 times the interference subspace dimension. This relationship is not consistent.

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时空自适应处理中的二次数据支持

时空自适应处理(STAP)技术应用于雷达的一个主要问题是干扰加噪声协方差矩阵估计的二次数据支持问题。Reed(1974)已经表明，在3db内实现最佳SINR的性能所需的辅助数据支持大约是自由度(DOF)的两倍。Reed在样本矩阵反演(SMI)技术中证明了这一规则。当将此规则应用于不属于SMI保护伞的一类较新的降维STAP算法时，会出现一个问题。本文的重点是交叉光谱度量(CSM)算法(Goldstein和Reed, 1997)。通过蒙特卡罗模拟，对非smi技术的样本支持度Reed规则进行了检验。通过选择与干涉子空间维数相等的自由度数来获得CSM算法的最优信噪比性能。在这种选择下，协方差矩阵估计所需的样本支持大约是干涉子空间维数的2.5倍。这种关系并不一致。

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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Proceedings of the 1998 IEEE Radar Conference, RADARCON'98. Challenges in Radar Systems and Solutions (Cat. No.98CH36197)

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