Sparse array optimization method based on Bayesian compressed sensing algorithm

Abstract

The invention discloses a Bayesian-compressed-sensing-algorithm-based sparse array optimization method. The method comprises the following steps: (1), converting an array sparse problem into a Bayesian probability matching problem of a reference beam of an array, and solving a correlation vector machine to obtain an initial sparse array; (2), performing first-order Taylor approximate expansion onthe obtained initial sparse array to increase a position offset of the array element position, and optimizing the sparse array with the increased position offset; and (3), defining a minimum array element spacing value, combining array element points of which the spacing is smaller than the minimum array element spacing value to achieve the purpose of constraining the minimum array element spacing, and finally calculating a weight coefficient of an array element through a convex optimization technology. The sparse array optimization method has relatively high calculation efficiency, the same beam pattern performance is obtained by adopting a smaller number of transducers; meanwhile, the minimum spacing of array elements of the sparse array is constrained on a reasonable numerical value.

Description

technical field [0001] The invention relates to the field of sonar array optimization, in particular to a sparse array optimization method based on Bayesian Compressive Sensing (BCS) algorithm. Background technique [0002] In recent years, underwater acoustic imaging technology has developed rapidly due to its application in underwater physics, biology, and geology. Among different underwater acoustic imaging techniques, phased-array 3D imaging sonar can acquire 3D information of a scene by using a beam pattern with appropriate side lobes, null position, main lobe size and shape, and directivity. Usually, array elements are distributed evenly at equal intervals. In order to obtain high-resolution images, a large number of arrays are required, which leads to high hardware cost and high computational complexity. Using sparse array synthesis technology, by removing part of the transducers in the full array of receiving transducers, and optimizing the position and weight of th...

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Problems solved by technology

Usually the array elements are distributed evenly at equal intervals. In order to obtain high-resolution images, a large number of arrays are required, which leads to high hardware costs and high computational complexity.

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