Abstract
The performance of standard MVDR algorithm would be decreased sharply when the sampled data is small. In this paper, vector MVDR algorithm and the diagonal loading are proposed to improve the robustness. Firstly, the sample date covariance matrix is whitened to obtain more uniform noise, and then use the standard deviation of covariance matrix as the loading amount to complete diagonal loading. Finally, the effectiveness and robustness of the proposed algorithm is verified by simulation. Simulation results reveal that it has higher positioning accuracy with small sample, with the increase of SNR and snapshots, the estimation’s RMSE of target location decreases continuously.
Highlights
Unmanned underwater vehicle (UUV) has become the important equipment for all countries, especially with the development of energy and control technology
The diagonal loading and minimum variance distortionless beamforming (MVDR) algorithm are applied to the near-field acoustic source localization with small sample, so as to improve the survival ability of mines
The root mean square error (RMSE) obtained by PMVDR algorithm and the CRB decrease with the increase of SNR, and the RMSE curve is constantly closed to CRB
Summary
Unmanned underwater vehicle (UUV) has become the important equipment for all countries, especially with the development of energy and control technology. It is very important to improve the localization ability of near field target with the finite sample date [1, 2]. When the number of samples is small, the sample covariance matrix will be an ill-conditioned matrix and the localization performance of MVDR algorithm will greatly reduce. Carlson proposed diagonal loading technique to correct the covariance matrix, improved the stability of the MVDR algorithm [4]. The diagonal loading and MVDR algorithm are applied to the near-field acoustic source localization with small sample, so as to improve the survival ability of mines
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