一种稳健的知识辅助STAP色加载系数优化算法

doi:10.11999/JEIT151335

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Abstract

In colored-loading Knowledge Aided STAP (KA-STAP) techniques, the colored-loading factor should be determined according to the performance of the a priori information. The existing Pre-Whitening (PW) colored-loading factor optimization method can not evaluate the accuracy degree of the a priori information of the Cell Under Test (CUT), which makes it not robust to the situation where a priori information for each range bin is different. In this paper, a colored-loading factor optimization method, CUT information involved PW (CPW), is proposed to improve the performance of PW method. In CPW, partial training samples are utilized to evaluate the pre-whitening ability of the colored-loading matrix of CUT. At the same time, non-uniqueness problem of the optimization result of PW is also solved. Simulations are conducted to discuss the performance of CPW under different sample support conditions and different a priori information performance situations. Simulation results demonstrate the effectiveness and robustness of the proposed CPW approach.

Key words： Knowledge Aided STAP (KA-STAP) Colored-Loading factor (CL) Pre-Whitening (PW)

Received: 26 November 2015 Published: 09 May 2016

PACS:

TN957.51

Fund:

The National Natural Science Foundation of China (61371184, 61301262, 61401062)

Corresponding Authors: ZHANG Shengmiao E-mail: miaomiao871@sina.com

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	ZHANG Shengmiao
	HE Zishu
	LI Jun
	ZHAO Xiang

Cite this article:

ZHANG Shengmiao,HE Zishu,LI Jun等. A Robust Colored-loading Factor Optimization Approach for KA-STAP[J]. JEIT, 2016, 38(8): 1942-1949.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT151335 OR http://jeit.ie.ac.cn/EN/Y2016/V38/I8/1942

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