振速轴向不一致下矢量传感器阵列方位估计方法

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摘要针对振速轴向不一致时矢量传感器阵列的方位估计精度不高问题，提出了一种两步加权交替迭代自适应方法(two-step weighted alternating iterative approach, TWAIA)。在矢量传感器阵列稀疏信号模型中引入轴向角度偏差参数，并把重构的干扰加噪声协方差矩阵作为加权项，基于加权协方差矩阵拟合和加权最小二乘分别构建了关于稀疏信号功率和轴向偏差矩阵的代价函数。首先固定轴向偏差矩阵，采用正则化加权稀疏协方差矩阵拟合方法估计稀疏信号功率；然后固定稀疏信号功率，采用正则化加权最小二乘估计轴向偏差矩阵，并根据轴向偏差在矩阵中的分布特性，重构期望的轴向偏差矩阵，以此交替的方式迭代更新稀疏信号功率和轴向偏差矩阵，直到被估计的稀疏信号功率相较于前一次的迭代值不再变化为止。最终通过对估计的稀疏信号功率谱峰搜索即可实现声源的波达方向估计。仿真结果表明，相较于现有方位估计方法，提出的TWAIA提高了振速轴向不一致时矢量传感器阵列的方位估计精度。

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	王伟东1
	李向水1
	谭伟杰2
	邹波蓉1
	李辉1

关键词 ：矢量传感器阵列, 加权交替迭代, 轴向偏差矩阵, 方位估计

Abstract：To improve the direction of arrival accuracy via the vector sensor array in the presence of velocity axial inconsistency, a two-step weighted alternating iterative approach(TWAIA) is proposed to estimate the DOA. First, the axial angle bias parameter is introduced into the signal model of the vector sensor array, and the reconstructed interference plus noise covariance matrix is used as the weighting item. Then, based on the weighted covariance matrix fitting and weighted least squares, the cost functions with respect to the sparse signal power and the axial angle bias matrix are reconstructed. In the first step, fix the axial angle bias matrix, the regularized weighted sparse covariance matrix fitting method is used to estimate the sparse signal power; In the second step, fix the sparse signal power, the regularized weighted least squares is employed to estimate the axial angle bias matrix. According to the bias distribution characteristics in the axial angle bias matrix, the desired axial angle bias matrix is reconstructed, and then both the sparse signal power and the axial angle bias matrix are iteratively updated in an alternating way until the estimated sparse signal power is no longer changed compared with the result of the previous iterative estimation. Finally, the DOA is obtained by searching the estimated sparse signal power spectrum peak. Simulation results show that, compared with the existing methods, the proposed TWAIA improves the DOA estimation accuracy of the vector sensor array under the condition of velocity axial inconsistency.

Key words： Vector sensor array weighted alternating iterative axial angle bias matrix direction of arrival estimation

收稿日期: 2021-10-27 出版日期: 2022-06-28

引用本文:

王伟东1，李向水1，谭伟杰2，邹波蓉1，李辉1. 振速轴向不一致下矢量传感器阵列方位估计方法[J]. 振动与冲击, 2022, 41(12): 283-292.
WANG Weidong1, LI Xiangshui1, TAN Weijie2, ZOU Borong1, LI Hui1. Direction of arrival estimation approach via a vector sensor array under velocity axial inconsistency. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(12): 283-292.

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http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2022/V41/I12/283

[1] 郑舒予, 张小宽, 宗彬锋. 基于改进MUSIC算法的散射中心参数提取及RCS重构[J]. 系统工程与电子技术, 2020, 042(001):76-82.
ZHENG S Y, ZHANG X K, ZONG B F. Extraction of scattering center parameters and reconstruction of RCS based on improved MUSIC algorithm[J]. Systems Engineering and Electronics, 2020, 42(1): 76-82.
[2] CAO J, LIU J, Wang J, et al. Acoustic vector sensor: reviews and future perspectives[J]. IET Signal Processing, 2017, 11(1): 1-9.
[3] AKTAS M, OZKAN H. Acoustic direction finding using single acoustic vector sensor under high reverberation[J]. Digital Signal Processing, 2018, 75: 56-70.
[4] NAJEEM S, KIRAM K, MALARKODI A, et al. Open lake experiment for direction of arrival estimation using acoustic vector sensor array[J].Applied Acoustics, 2017, 119: 94-100.
[5] NEHORAI A, PALDI E. Acoustic vector sensor array processing[J]. IEEE Transactions on signal processing, 1994, 42(9): 2481-2491.
[6] 马伯乐, 朱世强, 孙贵青. 一种声矢量阵最小方差无畸变方位估计算法[J]. 兵工学报, 2019, 40(1):153-158.
MA B L, ZHU S Q, SUN G Q. A minimum variance distortionless azimuth estimation algorithm for acoustic vector array[J]. Journal of Military Engineering, 2019, 40(1): 153-158.
[7] ZHANG X, XU L, XU L, et al. Direction of departure and direction of arrival estimation in MIMO radar with reduced dimension MUSIC [J].IEEE communications letters, 2010, 14(12): 1161-1163.
[8] ROY R, KAILATH T. ESPRIT-estimation of signal parameters via rotational invariance techniques[J]. IEEE Transactions on acoustics, speech, and signal processing, 1989, 37(7): 984-995.
[9] ZHAO A, MA L, HUI J, et al. Open-lake experimental investigation of azimuth angle estimation using a single acoustic vector sensor[J]. Journal of Sensors, 2018: 1-11.
[10] 王绪虎，陈建峰，韩晶，等. 基于ESPRIT算法的矢量水听器阵方位估计性能分析[J]. 系统工程与电子技术，2013，35(3): 481-486.
WANG X H, CEN J F, HAN J, et al. Performance analysis of vector hydrophone array azimuth estimation based on ESPRIT algorithm[J]. Systems Engineering and Electronic Technology, 2013, 35 (3): 481-486.
[11] CHEN H, ZHU W P, SWAMY M N S. Real-valued ESPRIT for two-dimensional DOA estimation of noncircular signals for acoustic vector sensor array[C]//2015 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2015: 2153-2156.
[12] LIU A, YANG D, SHI S, et al. Augmented subspace MUSIC method for DOA estimation using acoustic vector sensor array[J]. IET Radar, Sonar & Navigation, 2019, 13(6): 969-975.
[13] THOMPSON J S, GRANT P M, MMUGREN B. Performance of spatial smoothing algorithms for correlated sources[J]. IEEE Transactions on Signal Processing, 1996, 44(4): 1040-1046.
[14] TAO J W , CHANG W X , SHI Y W. Direction-finding of coherent sources via 'particle-velocity-field smoothing'[J]. Iet Radar Sonar and Navigation, 2008, 2(2):127-134.
[15] HE J, JIANG S Li, WANG J T, et al. Particle elocity field difference smoothing for coherent source localization in spatially nonuniform noise[J]. IEEE Journal of Oceanic Engineering, 2010, 35(1):113-119.
[16] 马伯乐，程锦房. 改进声矢量阵相干信号源方位估计算法[J]. 系统工程与电子技术，2016, 38(3): 519-524.
MA B L, CHENG J F. Improved algorithm for DOA estimation of coherent signal sources based on acoustic vector array [J]. Systems Engineering and Electronic Technology, 2016 , 38(3): 519-524.
[17] MALITOUTOV D, CETIN M, WILLSKY A S. A sparse signal reconstruction perspective for source localization with sensor arrays[J]. IEEE transactions on signal processing, 2005, 53(8): 3010-3022.
[18] YARDIBI T, LI J, STOICA P, et al. Source localization and sensing: A nonparametric iterative adaptive approach based on weighted least squares[J]. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(1): 425-443.
[19] 梁国龙, 马巍, 范展, 等. 矢量声纳高速运动目标稳健高分辨方位估计[J]．物理学报，2013，62(14)：274-282．
LIANG G L, MA W, FAN Z. Robust high-resolution DOA estimation of high speed moving targets in vector sonar [J]. Journal of Physics, 2013, 62 (14): 274-282.
[20] SHI S, LI Y, YANG D, et al. Sparse representation based direction of arrival estimation using circular acoustic vector sensor arrays[J]. Digital Signal Processing, 2020, 99 : 102675.
[21] 王伟东, 张群飞, 史文涛, 等. 基于稀疏信号功率迭代补偿的矢量传感器阵列DOA估计[J]. 振动与冲击, 2020, 39(15) : 48-57.
WANG W D, ZHANG Q F, SHI W T, et al. DOA estimation of vector sensor array based on sparse signal power iterative compensation [J]. Vibration and shock, 2020, 39 (15): 48-57.
[22] WANG W D, TAN W J. Alternating iterative adaptive approach for DOA estimation via acoustic vector sensor array under directivity bias[J]. IEEE Communications Letters, 2020, 24(9) :1944 -1948.
[23] SUN Y, BABU P, PALOMAR D P. Majorization-minimization algorithms in signal processing, communications, and machine learning[J]. IEEE Transactions on Signal Processing, 2016, 65(3): 794-816.
[24] TAN X, ROBERTS W, LI J, et al. Sparse learning via iterative minimization with application to MIMO radar imaging[J]. IEEE Transactions on Signal Processing, 2010, 59(3): 1088-1101.
[25] 张贤达. 矩阵分析与应用[M].第二版. 北京：清华大学出版社，2103.54-59.
ZhANG X D. Matrix analysis and application [M]. Second edition. Beijing: Tsinghua University Press, 2103.54-59.
[26] Abeida H, Zhang Q, Li J, et al. Iterative sparse asymptotic minimum variance based approaches for array processing[J]. IEEE Transactions on Signal Processing, 2012, 61(4): 933-944.
[27] XU Z, ChANG X, XU F, et al. regularization: A thresholding representation theory and a fast solver[J]. IEEE Transactions on neural networks and learning systems, 2012, 23(7): 1013-1027.