Efficient tapering methods for STAP

S. Pillai, J. Guerci, S. R. Pillai
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引用次数: 1

Abstract

The sample support problem in space-time adaptive processing (STAP) arises from the requirement to adapt to a changing interference environment where the available wide-sense-stationary sample support is severely limited for direct implementation of adaptive algorithms. In this paper we outline several approaches to address the sample support problem by utilizing efficient covariance matrix tapering (CMT) methods to retain the a-priori known structure of the covariance matrix. By combining efficient tapering approaches along with terrain knowledge based STAP and other preprocessing schemes such as subarray - subpulse, relaxed projection method, it is possible to reduce the data samples required in a nonstationary environment and consequently achieve superior target detection. In addition, the application of Khatri-Rao product to the data domain implementation of CMT is also introduced thus expanding the class of robust algorithms for real-time STAP implementation.
STAP的有效锥形方法
时空自适应处理(STAP)中的样本支持问题是由于需要适应不断变化的干扰环境而产生的,在这种环境中,可用的广义平稳样本支持严重限制了自适应算法的直接实现。在本文中,我们概述了几种解决样本支持问题的方法,利用有效的协方差矩阵变细(CMT)方法来保留协方差矩阵的先验已知结构。通过将有效的锥形方法与基于地形知识的STAP以及其他预处理方案(如子阵列-子脉冲、松弛投影法)相结合,可以减少非平稳环境下所需的数据样本,从而实现更好的目标检测。此外,还介绍了Khatri-Rao积在CMT数据域实现中的应用,从而扩展了实时STAP实现的鲁棒算法类。
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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