Three-dimensional camera shooting sonar system transducer array sparse optimization method considering near-field and far-field performance

Abstract

The invention discloses a three-dimensional camera shooting sonar system transducer array sparse optimization method considering a near-field and far-field performance. The method comprises the following steps of determining a focusing range error parameter of a three-dimensional camera shooting sonar system according to the type of a near-field digital beam forming algorithm in the three-dimensional camera shooting sonar system; determining a near-field sparse optimization energy function by utilizing the focusing range error parameter; solving by adopting a global optimization algorithm to make the near-field sparse optimization energy function achieve a minimum sparse transducer array Q1; carrying out secondary sparse optimization on the opened transducers in the Q1 and solving to make a far-field sparse optimization energy function achieve a minimum sparse transducer array Q2; when the sonar system works in a near-field state, carrying out focusing beam forming by using the Q1; and when the sonar system works in a far-field state, carrying out far-field beam forming by using the Q2. By using the method, the stable detection performance under different detection distances for the system can be ensured while the system hardware complexity is effectively reduced.

Description

technical field [0001] The invention relates to the technical field of phased array three-dimensional imaging sonar, in particular to a sparse optimization method for a transducer array of a three-dimensional imaging sonar system that takes both near-field and far-field performance into consideration. Background technique [0002] The high-resolution phased array 3D camera sonar system faces two major problems: 1) The number of array elements in the two-dimensional transducer array used to receive the acoustic echo signal is huge, and the system hardware complexity is very high; 2) The system hardware complexity is very high; Real-time signal processing is computationally intensive. [0003] In order to solve these two problems, the sparse transducer array used in the prior art can effectively reduce the number of transducers while ensuring the angular resolution, reduce the complexity of the system hardware, and greatly reduce the calculation amount of the real-time beamfor...

AI Technical Summary

Problems solved by technology

In the specific engineering implementation, the focusing delay used by the focused beamforming usually has a certain deviation, which leads to the performance degradation of the sparse array

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