Block Toeplitz with Toeplitz block covariance matrix for space-time adaptive processing

Youming Li, Chee Hoo Cheong
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引用次数: 4

Abstract

Interference covariance matrix estimations and computational complexity are two main concerns in space-time adaptive processing (STAP). This paper deals with the two problems by exploring a structured covariance matrix. First, a conjugate gradient iterative algorithm (CGIA) with reduced computational complexity is presented, which is based on FFT by using a block Toeplitz with Toeplitz block (BTTB) structure of the interference covariance matrix. To ensure the convergence of CGIA, an iterative BTTB (IBTTB) covariance matrix approximation is also proposed. Based on the approximated BTTB matrix, the corresponding STAP algorithm provides superior and robust performance both in limited sample support and in the presence of system errors.
用Toeplitz分块协方差矩阵进行时空自适应处理
干扰协方差矩阵估计和计算复杂度是空时自适应处理(STAP)中的两个主要问题。本文通过探索一个结构化协方差矩阵来解决这两个问题。首先,利用干涉协方差矩阵的块Toeplitz与Toeplitz块(BTTB)结构,提出了一种基于FFT的降低计算复杂度的共轭梯度迭代算法(CGIA)。为了保证CGIA的收敛性,提出了一种迭代BTTB (IBTTB)协方差矩阵逼近方法。基于近似的BTTB矩阵,相应的STAP算法在有限样本支持和存在系统误差的情况下都具有优越的鲁棒性。
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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