采用压缩感知的阵列失效单元诊断方法

doi:10.3969/j.issn.1001-2400.2018.02.027

西安电子科技大学学报

采用压缩感知的阵列失效单元诊断方法

李玮1,2;邓维波1,2;杨强1,2;索莹1,2;MIGLIORE Marco Donald3

(1. 哈尔滨工业大学电子与信息工程学院，黑龙江哈尔滨 150001;
2. 信息感知技术协同创新中心，黑龙江哈尔滨 150001;
3. 意大利卡西诺大学电信与信息工程学院，意大利卡西诺 03043)

收稿日期:2017-06-22 出版日期:2018-04-20 发布日期:2018-06-06
作者简介:李玮(1988-)，男，哈尔滨工业大学博士研究生，E-mail: hit_14B905002@163.com
基金资助:
哈尔滨工业大学博士生国外短期访学资助项目(AUDQ9802200116)；国家自然科学基金青年基金资助项目(61501145)；中央高校基本科研业务费专项资金资助项目(HIT.MKSTISP. 201613)

Diagnosis method for defective array elements based on compressive sensing

LI Wei1,2;DENG Weibo1,2;YANG Qiang1,2;YSUO Ying1,2;MIGLIORE Marco Donald3

(1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China;
2. Collaborative Innovation Center of Information Sensing and Understanding, Harbin 150001, China;
3. School of Telecommunications and Information Engineering, Univ. of Cassino, Cassino 03043, Italy)

Received:2017-06-22 Online:2018-04-20 Published:2018-06-06

摘要/Abstract

摘要：

针对现有阵列单元故障诊断方法随阵元数目增多而存在的采样数量大、诊断时间长、计算复杂度高等缺陷，提出了一种采用压缩感知理论的故障诊断方法．该方法基于故障单元数目固有的稀疏性，利用完好阵列和实际阵列激励的差值构造稀疏信号．根据目标方位信息设计测量矩阵的网格划分准则，并通过测量矩阵以随机欠采样方式获取少量测量数据．结合平行坐标下降算法对该稀疏信号进行精确重构，从而实现故障单元的准确诊断．理论分析和仿真实验表明，文中提出的方法不仅明显减少了采样数量，有效缩短了诊断时间，大幅降低了计算复杂度，而且进一步提高了故障信息的重构精度．

关键词: 阵列诊断, 压缩感知, 傅里叶变换子矩阵, 稀疏恢复, 迭代收缩算法

Abstract:

As the number of elements of the array increases, huge sampling data, long measurement time and high computation costs are several features of existing array fault diagnosis methods. Therefore, a diagnosis approach based on Compressive Sensing is investigated in this paper. The proposed method utilizes the sparsity of the number of failed elements and the sparse signal derives from difference incentives of reference array and the array under test. The criterion for grid division of the measurement matrix is designed according to the target direction in the spatial domain and a small number of measurement data are then obtained in the far field radiation pattern via a random under-sampling strategy. The Parallel Coordinate Decent Algorithm is used to implement fault diagnosis by reconstructing this sparse signal. Theoretical analysis as well as simulation results indicate that the proposed method not only reduces the amount of spatial sampling data, truncates the diagnosis time and abates the computational complexity significantly , but also improves the accuracy of recovered information on defective elements.

Key words: array diagnosis, compressive sensing, fourier transform submatrix, sparse recovery, iterative shrinkage algorithm

李玮;邓维波;杨强;索莹;MIGLIORE Marco Donald. 采用压缩感知的阵列失效单元诊断方法[J]. 西安电子科技大学学报, 2018, 45(2): 160-165.

LI Wei;DENG Weibo;YANG Qiang;YSUO Ying;MIGLIORE Marco Donald. Diagnosis method for defective array elements based on compressive sensing[J]. Journal of Xidian University, 2018, 45(2): 160-165.

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采用压缩感知的阵列失效单元诊断方法

Diagnosis method for defective array elements based on compressive sensing

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