基于自适应粒子群算法的目标位置测量方法

针对现有目标位置测量算法复杂和标准粒子群算法收敛速度慢的缺点,提出了一种基于自适应粒子群算法的目标位置测量方法。该方法通过自适应惯性权重平衡PSO的全局搜索和局部改良能力,通过自适应变异策略提高算法摆脱局部极值和局部寻优的能力,实现了目标位置的快速测量。仿真结果表明,该方法在测量时差精度为100ns的条件下,目标位置测量精度小于100m,且能够有效地避免早熟收敛问题,具有较快的收敛速度。

A target location measurement method based on an adaptive particle swarm optimization algorithm

A target location measurement method based on an adaptive particle swarm optimization algorithm is presented in view of the shortcoming of the existing target position measurement and the standard particle swarm optimizer algorithm,which has a complex calculation and low convergence rate.Measuring a target location fastly has been realized by introducing the adaptive inertia weight to balance global and local search ability,and the adaptive mutation strategy to improve the abilities of escaping from local optima and conducting local search.The simulation results show that the proposed algorithm can achieve the location measurement accuracy of 100 m on the condition that the measured time difference accuracy is 100 ns,avoid the premature convergence problem effectively,and have much faster convergence rate.