Sparse planar array optimizing method for energy transducers of phased array sonar system

Abstract

The invention provides a sparse planar array optimizing method for energy transducers of a phased array sonar system. The method comprises the steps of 1) initialization, 2) simulated annealing optimization, 3) teaching and learning optimization and 4) termination. According to the method, the minimal number of energy transducers, which need to be exited to satisfy the spatial response performance, of the sparse planar array is solved by utilizing spatial response performances including the main-lobe width and the side-lobe peak value of the array. The feasible solution obtained by the simulated annealing optimization can be further optimized via teaching and learning optimization, so that higher convergence speed and precision are provided, the optimal solution of the whole situation can be obtained, as few as energy transducers work, the required spatial response performance of the planar array of the energy transducers is obtained, and the required hardware cost of the phased array imaging sonar system can be effectively reduced.

Description

technical field [0001] The invention belongs to the technical field of phased array imaging sonar beam formation, and in particular relates to a method for optimizing a sparse array of transducers in a phased array sonar system. Background technique [0002] The phased array three-dimensional forward-looking sonar system is an underwater application system developed in recent years. The system uses a single-frequency sound pulse to transmit the entire observation scene, and uses phased array technology to simultaneously generate tens of thousands of real-time beam intensity signals. Real-time signal processing results in an image of a three-dimensional scene. Modern high-resolution phased-array sonar systems require transducer arrays with longer geometric dimensions in order to obtain smaller main beam widths. At the same time, in order to prevent the appearance of grating lobes, the array element spacing (the distance between the transducer and the transducer) should not b...

AI Technical Summary

Problems solved by technology

[0009] At present, the sparse array optimization of transducers mainly adopts population evolution algorithms such as legacy algorithm and population particle optimization algorithm, which are easy to fall into local optimum prematurely and have slow convergence speed. They are often used to optimize one-dimensional transducer linear arrays or small area arrays. Suitable for phased array imaging sonar systems with a large number of transducers

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