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Sparse Bayesian learning-based spatial spectrum estimation for mobile sonar platforms during turning
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AI
DOI：10.1016/j.apacoust.2022.108937.png)
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Abstract
En 中文
Mobile sonar platforms usually experience turning in the course of travel. During turning, due to the change of heading, the steering vector of the sonar array is not constant, which will cause the widening of the spatial-spectral peaks or even fail to estimate target bearings. To deal with this problem, this paper first establishes a multi-snapshot fusion equation for observed data from different heading angles. Then, a sparse Bayesian learning-based method is utilized to solve the fusion equation and provides the esti-mate of the spatial spectrum. The simulation results exhibit that the proposed method can resolve the port and starboard ambiguity problem and provide high estimation accuracy with enough heading change information. The sea trial results validate its feasibility and stability for estimating far-field target bearings in practical applications.(c) 2022 Published by Elsevier Ltd.
Keywords:
Mobile sonar platform
Port and starboard ambiguity
Spatial spectrum estimation
Sparse Bayesian learning