A far-near field hybrid source off-network positioning method based on sparse Bayesian learning

Abstract

The application discloses a far-near field mixed source off-grid positioning method based on sparse Bayesian learning, and belongs to the field of underwater acoustic detection.The application solves the problem that the existing method cannot realize the consideration of high positioning accuracy and high calculation efficiency.The application constructs a far-near field off-grid model, introduces far-near field off-grid error as a hyperparameter into a sparse Bayesian learning process, realizes effective estimation and compensation of the off-grid error, completes far-near field positioning with higher accuracy, and greatly reduces the influence of near-field strong interference on far-field direction finding.Meanwhile, the application utilizes far-near field grid evolution technology to realize autonomous splitting learning of far-near field grid points near the target position, so that the grid points can non-uniformly and with emphasis cover the space domain of interest, and the calculation efficiency of the method can be improved while the positioning accuracy is improved.The application method can be applied to the field of far-near field mixed source off-grid positioning.

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