一种稳健的知识辅助STAP色加载系数优化算法

张圣鹋; 何子述; 李军; 赵翔

doi:10.11999/JEIT151335

一种稳健的知识辅助STAP色加载系数优化算法

doi: 10.11999/JEIT151335

基金项目:

国家自然科学基金(61371184, 61301262, 61401062)

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- 文章访问数: 1501
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- 被引次数: 0
出版历程
- 收稿日期: 2015-11-26
- 修回日期: 2016-03-08
- 刊出日期: 2016-08-19

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

Funds:

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

摘要

摘要: 色加载知识辅助STAP技术中，需根据先验信息的准确度设置色加载系数。已有的基于色加载矩阵预白化性能评估的色加载系数优化算法(Pre-Whitening, PW)无法评估当前待检测距离单元(CUT)的先验信息准确度，对于杂波先验信息准确度不均匀的场景不具有鲁棒性。该文在PW法的基础上提出一种可有效评估CUT单元色加载矩阵性能的稳健色加载系数优化方法(CUT information involved PW, CPW)。CPW法利用部分参考单元样本实现对CUT单元色加载矩阵预白化能力的评估，同时解决了PW法优化结果非单值性的问题。仿真实验讨论了CPW法在不同参考单元样本个数以及不同先验信息准确度条件下的色加载系数优化性能。仿真结果验证了所提方法的有效性及稳健性。
- 知识辅助空时自适应处理 /
- 色加载系数 /
- 预白化
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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参考文献(22)

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