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Volume 38 Issue 8
Sep.  2016
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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
Citation: 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

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
  • 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.
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