Application on Target Localization Based on Salp Swarm Algorithm

To solve the nonlinear optimization problems of Time-Difference-Of-Arrival (TDOA) passive location, a novel target localization method based on Salp Swarm Algorithm(SSA) is proposed. Firstly, it adopts a new population update model to fully balance the exploration behavior and development behavior during the iteration process, which not only ensure the search of the overall situation and the diversity of individuals, but also improves the problem that other intelligent optimization algorithms are easily stagnate in local optima. Second, the algorithm has very few control parameters and the computing speed is obviously improved. Finally, the convergence speed of the algorithm is very stable and the positioning accuracy is higher. The simulation results show that the new algorithm can quickly and stably converge to the target position in TDOA passive location, compared with Particle Swarm Optimization(PSO) and Improved Particle Swarm Optimization(IPSO), SSA has significantly higher location accuracy.