Target Movement Parameter Estimation Based on Particle Swarm Optimization Algorithm

Considering the demand for real-time property and accuracy of an underwater target passive tracking and location system, a target parameter estimation method is proposed by using the target’s azimuth and Doppler shift information. With the measured target’s azimuth variation and Doppler frequency shift, a parameter estimation equation is established based on the minimum mean square error(MMSE) criterion, and a set of motion parameters is determined by the particle swarm optimization(PSO) algorithm to minimize the mean square error function, thus the accurate estimations of target’s real-time position, velocity, and closest passing distance are achieved. Simulation results show that the PSO algorithm converges more rapidly with equivalent convergence precision compared with the extended Kalman filter algorithm; and for the close-distance and high-speed target, the PSO algorithm can provide accurate prediction of target’s closest passing distance and high tracking precision before and after it passes by, respectively. This research is expected to provide a reference for passive tracking of underwater target and accurate estimation of movement parameters.