An Airborne Multi-Sensor Task Allocation Method Based on Improved Particle Swarm Optimization Algorithm

Abstract

The characteristics of airborne multi-sensor task allocation problem are analyzed, and an airborne multi-sensor task allocation model is established. In order to solve the problems of local convergence and slow convergence of the traditional Particle Swarm Optimization (PSO) algorithm, the structure and parameters of the existing Particle Swarm Optimization algorithm are adjusted, and the direction coefficient and far away factor are introduced to control the velocity and direction of the particle far away from the worst solution, so that the particle moves away from the worst solution while moving to the optimal solution. Based on the improved Particle Swarm Optimization algorithm, an airborne multi-sensor task allocation method is proposed using maximum detection probability as objective function, and the algorithm is simulated. The simulation results show that this algorithm can effectively allocate tasks and improve allocation effects.