基于稀疏恢复谱相似度的自适应样本筛选算法

doi:10.3969/j.issn.1001-506X.2018.05.03

系统工程与电子技术 ›› 2018, Vol. 40 ›› Issue (5): 976-981.doi: 10.3969/j.issn.1001-506X.2018.05.03

基于稀疏恢复谱相似度的自适应样本筛选算法

王晓明¹, 李军¹, 张圣鹋¹, 卢燕¹, 张永杰²#br#

#br#

1. 电子科技大学电子工程学院, 四川成都 611731; 2. 毫米波遥感技术重点实验室, 北京 100854

出版日期:2018-04-28 发布日期:2018-04-24

Adaptive sample selection algorithm based on sparse recovery spectral similarity

WANG Xiaoming¹, LI Jun¹, ZHANG Shengmiao¹, LU Yan¹, ZHANG Yongjie²

1. School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; 2. Science and Technology on MillimeterWave Laboratory, Beijing 100854, China

Online:2018-04-28 Published:2018-04-24

摘要/Abstract

摘要：

非均匀环境下的空时自适应处理算法需对参考单元样本进行筛选。针对小系统自由度下,已有的基于傅里叶谱相似度(Fourier spectral similarity, FSPS)的筛选算法在污染样本剔除以及相似样本选择环节都存在分辨率不足的问题,提出一种基于稀疏恢复技术的自适应样本筛选算法。该方法利用参考单元样本及待检测单元(cell under test, CUT)样本的高精度稀疏恢复谱筛选出与CUT杂波特征相近的样本,保留了FSPS算法在非均匀杂波环境下的鲁棒性,同时提升对非均匀样本的分辨精度。仿真结果表明,所提算法在小系统自由度情况下具有优于FSPS算法的样本筛选效果及系统输出性能。

Abstract:

Training sample selection is inevitable for space-time adaptive processing in a heterogeneous environment. The existing training sample selection algorithm based on Fourier spectral similarity (FSPS) cannot meet the resolution requirement in both the target signal contaminated sample discarding step and the similar sample selecting step when the system’s degree of freedom (DoF) is small. To mitigate this problem, this paper proposes an adaptive training sample selection algorithm based on the sparse recovery spectrum. This study utilizes the cell under test (CUT) and initial training samples’ high-precision sparse recovery spectrums to select samples which have similar clutter characteristics with the CUT. The proposed method enhances the resolution for heterogeneous samples compared with FSPS while shows the robustness in a heterogeneous environment. Simulation results demonstrate that the proposed method has better selection results and system output performance than the FSPS method under a low system’s DoF situation.

王晓明, 李军, 张圣鹋, 卢燕, 张永杰. 基于稀疏恢复谱相似度的自适应样本筛选算法[J]. 系统工程与电子技术, 2018, 40(5): 976-981.

WANG Xiaoming, LI Jun, ZHANG Shengmiao, LU Yan, ZHANG Yongjie. Adaptive sample selection algorithm based on sparse recovery spectral similarity[J]. Systems Engineering and Electronics, 2018, 40(5): 976-981.

基于稀疏恢复谱相似度的自适应样本筛选算法

Adaptive sample selection algorithm based on sparse recovery spectral similarity

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