邓维波1, 2,,,
杨强1, 2,
MIGLIOREMarco Donald3
1.哈尔滨工业大学电子与信息工程学院 ??哈尔滨 ??150001
2.对海监测与信息处理工业和信息化部重点实验室 ??哈尔滨 ??150001
3.意大利卡西诺大学电信与信息工程学院 ??卡西诺 ??03043
基金项目:哈尔滨工业大学博士生国外短期访学项目基金(AUDQ9802200116)，中央高校基本科研业务费专项资金(HIT.MKSTISP.2016 13, HIT.MKSTISP.2016 26)
详细信息
作者简介:李玮：男，1988年生，博士，研究方向为基于压缩感知的阵列诊断方法
邓维波：男，1961年生，教授，博士生导师，研究方向为阵列信号处理、雷达系统、压缩感知理论
杨强：男，1970年生，教授，博士生导师，研究方向为弱目标检测、新体制信号处理和信息提取、实时信号处理
MIGLIOREMarco Donald：MIGLIORE Marco Donald：男，1960年生，教授，博士生导师，研究方向为阵列诊断、天线测量、电磁场理论与技术、MIMO雷达
通讯作者:邓维波 dengweibo@hit.edu.cn
中图分类号:TN911.7计量
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被引次数:0
出版历程
收稿日期:2018-02-09
修回日期:2018-08-22
网络出版日期:2018-08-28
刊出日期:2018-11-01
Deterministic Compressed Sensing Sampling Strategy for Diagnosis of Defective Array Elements Using Far-field Measurements
Wei LI1, 2,Weibo DENG1, 2,,,
Qiang YANG1, 2,
Marco Donald MIGLIORE3
1. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2. Key Laboratory of Marine Environmental Monitoring and Information Processing, Ministry of Industry and Information Technology, Harbin 150001, China
3. School of Telecommunications and Information Engineering, University of Cassino, Cassino 03043, Italy
Funds:The Short-term Visiting Abroad Program for Doctoral Candidates of Harbin Institute of Technology (AUDQ9802200116), The Fundamental Research Funds for the Central Universities (HIT.MKSTISP.2016 13, HIT.MKSTISP.2016 26)
摘要
摘要:在采用压缩感知的阵列失效单元诊断方法中，结构化随机采样策略的运用对测量矩阵性能造成不利影响。针对这一问题，该文提出一种基于确定性压缩感知采样策略的阵列失效单元远场诊断方法。首先在失效单元个数满足稀疏性的前提下构造差异性阵列并将其激励作为稀疏向量，其次利用所提方法构造确定性部分傅里叶矩阵(DPFM)作为测量矩阵，最后采用l1范数最小化算法对稀疏向量进行重构，从而实现对失效单元的高概率精确诊断。理论分析和仿真实验表明，所提方法有效消除了采样位置的随机分布特性对测量矩阵性能造成的不利影响，简化了采样过程，提高了诊断成功概率。
关键词:阵列诊断/
压缩感知/
确定性部分傅里叶矩阵/
稀疏恢复/
天线测量
Abstract:The structured random sampling strategy adopted in array diagnosis has negative influence on the performance of measurement matrix. Therefore, a compressed sensing based deterministic sampling strategy to diagnose defective array elements using far-field measurements is investigated in this paper. In the case of the number of failed elements satisfies sparsity, the sparse vector is constructed by subtracting incentives of reference array without failures and the array under test. Deterministic Partial Fourier Matrix (DPFM) is then formulated by the proposed strategy as the measurement matrix. Finally, accurate diagnosis with high probability is achieved by l1 norm minimization. Theoretical analysis and simulation results demonstrate that the proposed method can avoid the adverse impact on the performance of measurement matrix effectively arising from the random distribution of sampling positions, simplify the sampling procedure and improve the probability of success rate of diagnosis.
Key words:Array diagnosis/
Compressed Sensing (CS)/
Deterministic Partial Fourier Matrix (DPFM)/
Sparse recovery/
Antenna measurement
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