Tensor Decomposition and K‑means Clustering Based Array Diagnosis for MIMO Radar in Impulsive Noise Environment

CHEN Jin-li; WANG YA-peng; LI Jia-qiang; LONG Wei-jun

doi:10.12263/DZXB.20210347

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Tensor Decomposition and K‑means Clustering Based Array Diagnosis for MIMO Radar in Impulsive Noise Environment

PAPERS | 更新时间：2025-12-08

- Tensor Decomposition and K‑means Clustering Based Array Diagnosis for MIMO Radar in Impulsive Noise Environment
- ACTA ELECTRONICA SINICA Vol. 49, Issue 12, Pages: 2315-2322(2021)
- 作者机构：
  
  1.南京信息工程大学气象灾害预报预警与评估协同创新中心，江苏南京 210044
  2.南京信息工程大学电子与信息工程学院，江苏南京 210044
  3.南京电子技术研究所，江苏南京 210039
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210347
  CLC： TN951;TN911.23
- Received：12 March 2021，
  
  Revised：2021-04-22，
  
  Published：25 December 2021
- 稿件说明：
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陈金立,王亚鹏,李家强等.冲击噪声下基于张量分解和K‑means聚类的MIMO雷达阵列诊断[J].电子学报,2021,49(12):2315-2322.

CHEN Jin-li,WANG YA-peng,LI Jia-qiang,et al.Tensor Decomposition and K‑means Clustering Based Array Diagnosis for MIMO Radar in Impulsive Noise Environment[J].ACTA ELECTRONICA SINICA,2021,49(12):2315-2322.
陈金立,王亚鹏,李家强等.冲击噪声下基于张量分解和K‑means聚类的MIMO雷达阵列诊断[J].电子学报,2021,49(12):2315-2322. DOI： 10.12263/DZXB.20210347.

CHEN Jin-li,WANG YA-peng,LI Jia-qiang,et al.Tensor Decomposition and K‑means Clustering Based Array Diagnosis for MIMO Radar in Impulsive Noise Environment[J].ACTA ELECTRONICA SINICA,2021,49(12):2315-2322. DOI： 10.12263/DZXB.20210347.

摘要

针对冲击噪声下多输入多输出(Multiple‑Input Multiple‑Output

MIMO)雷达阵列诊断失效问题，对基于二阶矩的传统匹配滤波器进行改进以适应非高斯噪声，并提出一种基于张量分解和K‑means聚类的阵列诊断方法. 该方法利用MIMO雷达各接收阵元回波信号的高斯核函数值来自适应地调整匹配滤波器的系数，以有效形成虚拟阵列. 为挖掘正常和故障阵元的匹配滤波输出数据的多维特征，将虚拟阵列协方差矩阵构建成三阶平行因子(PARAllel FACtor

PARAFAC)张量，并通过COMFAC(COMplex parallel FACtor analysis)算法分解获得收发阵列流形矩阵，使用欧式距离度量其相似性，确定两个簇类数据的聚类中心并划分出异常簇类，以完成故障阵元位置的诊断. 仿真结果验证了所提算法的有效性.

Abstract

The traditional array diagnosis methods for multiple-input multiple-output(MIMO) radar may fail in the presence of impulse noise. The traditional matched filter based on second-order statistics is modified to obtain a reliable performance in the non-Gaussian noise

and then the array diagnosis method based on tensor decomposition and K-means clustering is proposed. The coefficients of the matched filters are adjusted with the Gaussian kernel function values of the echo signal observed at each receive element

which makes the MIMO radar form a virtual array successfully in the presence of impulsive noise. To further utilize the inherent multidimensional structure of the matched filter output data of the damaged and normal antennas

a third-order parallel factor(PARAFAC) model of the virtual array covariance matrix is formulated. By exploiting the complex parallel factor analysis(COMFAC) algorithm on the third-order covariance tensor

the manifold matrices of the transmit and receive arrays are obtained. The similarity of manifold matrix data is measured using Euclidean distance

and the clustering centers of the two clusters corresponding to the normal and fault elements are determined. The abnormal cluster data is selected to diagnose the location of the fault elements in MIMO radar array. Numerical simulation results confirm the effectiveness of the proposed algorithm.

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references

Xi F , Xiang Y , Zhang Z , Chen S , et al . Joint angle and doppler frequency estimation for MIMO radar with one-bit sampling: a maximum likelihood-based method [J]. IEEE Transactions on Aerospace and Electronic Systems , 2020 , 56 ( 6 ): 4734 - 4748 .

张正言 , 张剑云 , 郑志东 , 等 . 低信噪比双基地MIMO雷达目标角度跟踪算法 [J]. 电子学报 , 2019 , 47 ( 12 ): 2480 - 2487 .

ZHANG Zheng-yan , ZHANG Jian-yun , ZHENG Zhi-dong , et al . Target angle tracking algorithm of bistatic MIMO radar with low signal-to-noise ratio [J]. Acta Electronica Sinica , 2019 , 47 ( 12 ): 2480 - 2487 . (in Chinese)

Liu C , Vaidyanathan P P . Robustness of difference coarrays of sparse arrays to sensor failures—part I: a theory motivated by coarray MUSIC [J]. IEEE Transactions on Signal Processing , 2019 , 67 ( 12 ): 3213 - 3226 .

孙兵 , 吴晨曦 , 阮怀林 , 叶文强 , 等 . 阵元失效条件下的高精度DOA估计方法 [J]. 电子学报 , 2020 , 48 ( 9 ): 1688 - 1694 .

SUN Bing , WU Chen-xi , RUAN Huai-lin , et al . High accuracy DOA estimation method with array sensor failure [J]. Acta Electronica Sinica , 2020 , 48 ( 9 ): 1688 - 1694 . (in Chinese)

Bucci O M , Migliore M D , Panariello G , et al . Accurate diagnosis of conformal arrays from near-field data using the matrix method [J]. IEEE Transactions on Antennas and Propagation , 2005 , 53 ( 3 ): 1114 - 1120 .

Migliore M D . A compressed sensing approach for array diagnosis from a small set of near-field measurements [J]. IEEE Transactions on Antennas & Propagation , 2011 , 59 ( 6 ): 2127 - 2133 .

Xie K , Ning X , Wang X , et al . Recover corrupted data in sensor networks: a matrix completion solution [J]. IEEE Transactions on Mobile Computing , 2017 , 16 ( 5 ): 1434 - 1448 .

Zhang T , Chen J , Chen X . Array diagnosis using signal subspace clustering in MIMO radar [J]. Electronics Letters , 2020 , 56 ( 2 ): 99 - 102 .

Shao M , Nikias C L . Signal processing with fractional lower order moments: stable processes and their applications [J]. Proceedings of the IEEE , 1993 , 81 ( 7 ): 986 - 1010 .

Alam M S , Kaddoum G , Agba B L . Bayesian MMSE estimation of a Gaussian source in the presence of bursty impulsive noise [J]. IEEE Communications Letters , 2019 , 22 ( 9 ): 1846 - 1849 .

Erdogmus D , Agrawal R , Principe J C . A mutual information extension to the matched filter [J]. Signal Processing , 2005 , 85 ( 5 ): 927 - 935 .

Pokharel P P , Liu W , Principe J C . A low complexity robust detector in impulsive noise [J]. Signal Processing , 2009 , 89 ( 10 ): 1902 - 1909 .

陈金立 , 王亚鹏 , 苗玉叶 , 等 . 冲击噪声下基于无穷范数互信息的MIMO雷达阵列诊断 [J]. 中国电子科学研究院学报 , 2020 , 15 ( 10 ): 49 - 56 .

CHEN Jin-li , Wang Ya-peng , Miao Yu-ye , et al . Infinite norm mutual information based array diagnosis for MIMO radar in impulsive noise environment [J]. Journal of CAEIT , 2020 , 15 ( 10 ): 49 - 56 . (in Chinese)

Xu W , Li B J , Ma R B , et al . A novel kernel correlation coefficient with robustness against nonlinear attenuation and impulsive noise [J]. Journal of Signal Processing Systems , 2017 , 89 ( 2 ): 395 - 413 .

Wang Z Y , Cai C X , Wen F Q , et al . A quadrilinear decomposition method for direction estimation in bistatic MIMO radar [J]. IEEE Access , 2018 , 6 : 13766 - 13772 .

Wen F Q , Zhang Z J , Zhang G . Joint DOD and DOA estimation for bistatic MIMO radar: a covariance trilinear decomposition perspective [J]. IEEE Access , 2019 , 7 : 53273 - 53283 .

Bro R , Sidiropoulos N D , Giannakis G B . A fast least squares algorithm for separating trilinear mixtures [A]. International Workshop on Independent Component Analysis and Blind Separation [C]. Aussois, France : ICA , 1999 . 11 - 15 .

Hua M , Lau M K , Pei J , et al . Continuous K-means monitoring with low reporting cost in sensor networks [J]. IEEE Transactions on Knowledge and Data Engineering , 2009 , 21 ( 12 ): 1679 - 1691 .

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