Application of particle swarm optimization with stochastic inertia weight and adaptive mutation in target localization

Target localization based on time difference of arrival (TDOA) measurements has important applications in sonar, radar and sensor networks. This paper simply introduced the target localization principle of moving emitter and the position location algorithm. Further more presented an improved particle swarm optimization with stochastic inertia weight and adaptive mutation, and adopts it to solve the target localization problem according to the batch of continuous TDOA measurements. The experimental results show that the new algorithm has higher localization accuracy, better algorithm stability and faster convergence rate.