Research on multi-target tracking of moving observation stations based on IL-PSO algorithm

Regarding how the positional arrangement of mobile sensors affects multi-target tracking accuracy, the interactive learning particle swarm optimisation (IL-PSO) algorithm is introduced. It uses observation stations' motion models to predict multi-target states. First, targets are allocated for effective tracking. Then, joint probabilistic data association (JPDA), strong tracking filter (STF), and genetic algorithm particle filter (GAPF) algorithms are integrated to improve tracking accuracy. A comparative analysis based on IL-PSO shows its superior performance in metrics like relative point position error. This is due to its optimisation of station positions and the integration of algorithms enhancing precision and robustness.