Vector-Sensor Array DOA Estimation Based on Spatio-Temporal Correlation Matrices Joint Diagonalization Using Jacobi Rotation

Author(s):  
Haiyan Song ◽  
Ming Diao ◽  
Tao Tang ◽  
Jinping Qin
2014 ◽  
Vol 530-531 ◽  
pp. 530-533
Author(s):  
Jin Fang Cheng ◽  
Chao Ran Zhang ◽  
Wei Zhang

The MUSIC algorithm cannot deal with the problem of DOA estimation of coherent sources, this paper proposes the USTC (unitary spatio-temporal correlation matrices)-MUSIC algorithm using single vector hydrophone to solve this problem, by utilizing the unitary spatio-temporal correlation matrix instead of the covariance matrix. The simulation results demonstrate that the USTC-MUSIC algorithm has a better ability to distinguish the coherent sources from different directions than the spatial smoothing MUSIC algorithm.


2014 ◽  
Vol 22 (7) ◽  
pp. 1969-1975
Author(s):  
李新波 LI Xin-bo ◽  
李晓青 LI Xiao-qing ◽  
刘国君 LIU Guo-jun ◽  
石要武 SHI Yao-wu ◽  
杨志刚 YANG Zhi-gang

2016 ◽  
Vol 95 (2) ◽  
pp. 1285-1297 ◽  
Author(s):  
Sheng Liu ◽  
Lisheng Yang ◽  
Yuanju Xie ◽  
Yanhong Yin ◽  
Qingping Jiang

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Peng Wang ◽  
Yujun Kong ◽  
Mingxing Zhang

In this paper, the errors of acoustic vector sensor array are classified, the impact factor of each error for the array signal model is derived, and the influence of each type of error on the direction-of-arrival (DOA) estimation performance of the array is compared by Monte Carlo experiments. Converting the directional error and location error to amplitude and phase errors, the optimization model and error self-calibration algorithm for acoustic vector sensor array are proposed. The simulation experiments and field experiment data processing of MEMS vector sensor array show that the proposed self-calibration algorithm has good parameter estimation performance and certain engineering practicability.


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