Underwater sound source matching field positioning method based on diffusion mapping

Abstract

The invention discloses an underwater sound source matching field positioning method based on diffusion mapping, and the method comprises the steps: (1) selecting distance and depth grids, and calculating a copy field vector and a normalized copy field vector matrix at each grid through a sound field model; (2) calculating a covariance matrix and carrying out matrix norm normalization; (3) constructing the copy field vector matrix and the covariance matrix into a matrix set, and calculating an index Riemannian distance between every two matrixes in the set; (4) constructing a diffusion kernel matrix according to the distance matrix, performing eigendecomposition, and constructing a diffusion mapping graph according to eigenvalues and eigenvectors; and (5) calculating the distance between the covariance matrix on the diffusion map and different copy field vector matrixes, wherein the position corresponding to the shortest distance is the sound source position. Compared with a conventional matching field algorithm, passive positioning can be effectively performed on a target sound source in a low signal-to-noise ratio environment; the side lobe level can be effectively reduced, and the main lobe resolution is improved.

Description

technical field [0001] The present invention relates to an underwater sonar array signal processing algorithm, in particular to an underwater sound source matching field positioning method based on diffusion mapping, which can be applied to passive sound sources of sonar arrays in low signal-to-noise ratio environments Positioning belongs to the field of underwater acoustic array signal processing. Background technique [0002] Matching field processing technology has become one of the main means of passive detection of target sound sources by combining the traditional array signal processing method with the acoustic field model based on the characteristics of the ocean environment. In this method, the sound source, the environment and the receiving array are taken as a whole system, and the sound source position can be estimated by correlating and matching the actual measured sound signal of the receiving array with the sound field predicted by the theoretical model. Howev...

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Problems solved by technology

However, considering the nonlinear characteristics of matched field processing, its output ambiguity indicates that complex multi-mode structures will appear, so its processing performance has an obvious threshold effect, that is, the matching field algorithm will decrease as the signal-to-noise ratio decreases. The mean square error gradually increases, which is manifested as sidelobe blurring; especially, under the condition of very low signal-to-noise ratio, the performance of the algorithm is seriously degraded, and the detection and positioning of the target cannot be realized

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