Application of simulated annealing particle swarm optimization in underwater acoustic positioning optimization

Underwater acoustic positioning system will eventually lead to the estimation error of underwater target position measurement due to factors such as array position measurement error, time delay measurement error, attitude accuracy, sound velocity measurement error and so on. Therefore, the optimal solution method in underwater acoustic positioning system is also one of the key technologies in the field of underwater acoustic positioning technology. The traditional statistical methods of target position estimation mostly use arithmetic average optimization method, Newton iteration method and so on. In this paper, the particle swarm optimization (PSO) algorithm based on simulated annealing is used to estimate the target position, which can improve the estimation accuracy of traditional underwater acoustic location algorithm. In this paper, the statistical methods of three optimal solutions are analyzed by computer simulation. It is verified that the particle swarm optimization method based on simulated annealing can better estimate the target location in low signal-to-noise ratio. In the public literature at home and abroad, arithmetic average optimization method, quality factor weighting method and Newton iteration method are often used to estimate the optimal solution.