基于改进自适应粒子群算法的目标定位方法

Research on Target Localization Based on Improved Adaptive Velocity Particle Swarm Optimization Algorithm

针对现有目标定位求解算法推导复杂和自适应粒子群算法仍存在收敛速度慢、计算量大的缺点,提出了一种基于速度自适应和变异自适应融合的改进粒子群算法。该算法在速度自适应粒子群算法的基础上,优化选择粒子,并根据种群适应度方差值进行自适应变异,增强算法快速收敛的能力。仿真结果表明该方法能有效地提高目标定位精度,在随机噪声干扰方差为0.5的条件下,定位均方误差不超过1.5m,且收敛速度增快,计算量减小。

An improved adaptive particle swarm optimization algorithm based on velocity adaption and mutation adaption was proposed in view of the shortcoming of the existing localization algorithm and standard particle swarm optimizer algorithm,which has complex calculation,convergence speed and large computational load.The method has selected the particle swarm on the adaptive velocity particle swarm optimization algorithm,added adaptive mutation operation in iteration process to enhance its ability of quick convergence,and the mutation probability is adaptively adjusted by variance of the population＇s fitness.The simulation results indicate that it could carry on the localization effectively through adopting the improved adaptive particle swarm optimization algorithm.when the variance of random noise interference is 0.5,the localization RMSE is below 1.5m,and has high convergence speed and low computational load.