基于自适应粒子群算法的目标定位方法研究

Research on Target Localization Based on Adaptive Particle Swarm Optimization Algorithm

针对现有定位求解算法复杂和标准粒子群算法易陷入局部最优的缺点,提出了一种基于自适应粒子群算法的目标定位方法.该方法在迭代过程中指数更新惯性权重,择优选择粒子,并根据种群适应度方差值自适应地调整变异概率的大小,增强算法跳出局部最优的能力.仿真结果表明该方法能有效地提高目标的定位精度,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0.8m.

A new method of target localization based on adaptive particle swarm optimization algorithm is proposed in view of the shortcoming of the existing localization algorithm and standard particle swarm optimizer algorithm,which has a complex calculation and is easy to be trapped in local optimum.The method has improved the inertia weight of the standard particle swarm optimizer algorithm,selected the particle swarm,and added adaptive mutation operation in iteration process to enhance its ability to break away from localoptimum,which 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 adaptive particle swarm optimization algorithm.when the variance of random noise interference is 0.5,the localization RMSE is below 0.8 m.