A Robust Colored-loading Factor Optimization Approach for KA-STAP

ZHANG Shengmiao; HE Zishu; LI Jun; ZHAO Xiang

doi:10.11999/JEIT151335

Volume 38 Issue 8

Sep. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(8): 1942-1949

Xu Shao-Kun, Liu Ji-Hong, Yuan Xiang-Yu, Lu Jing. Two Dimensional Geometric Feature Inversion Method for Midcourse Target Based on ISAR Image[J]. Journal of Electronics & Information Technology, 2015, 37(2): 339-345. doi: 10.11999/JEIT140338

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ZHANG Shengmiao, HE Zishu, LI Jun, ZHAO Xiang. A Robust Colored-loading Factor Optimization Approach for KA-STAP[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1942-1949. doi: 10.11999/JEIT151335

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A Robust Colored-loading Factor Optimization Approach for KA-STAP

doi: 10.11999/JEIT151335

Funds:

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

Received Date: 2015-11-26
Rev Recd Date: 2016-03-08
Publish Date: 2016-08-19

Abstract

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.
- Knowledge Aided STAP (KA-STAP),
- Colored-Loading factor (CL),
- Pre-Whitening (PW)

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References(22)

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