一种基于KL-AEPF的无人机侦察移动目标定位算法

A KL-AEPF-based UAV reconnaissance moving target localization algorithm

基于EPF的无人机侦察移动目标定位算法在采样阶段需要利用EKF算法计算所有粒子的均值和协方差,导致其计算量大。本文提出了一种基于KL距离的自适应EPF改进算法,该方法在采样阶段利用EKF算法更新前半部分粒子,后半部分粒子仍通过先验概率分布更新,然后根据两个粒子集概率分布间的KL距离自适应更新当前时刻的粒子数。在保证精度的同时选择合适的粒子数目,大幅度降低计算量,提高运算速度。通过实测飞行数据验证,该算法平均每个采样周期内粒子数为40,平均每个采样周期内计算时间为8 ms。与EPF算法相比,该方法能在保证定位精度的同时明显减少计算耗时,具有一定的工程应用价值。

EPF-based UAV reconnaissance moving target localization algorithm needs to use the EKF algorithm to calculate the mean and covariance of all particles in the sampling stage,which results in a large amount of computation.In this paper,an improved adaptive EPF algorithm based on KL divergence is proposed.The method uses the EKF algorithm to update the first half of the particles in the sampling phase.The latter half of the particles is still updated with the prior probability distribution,and then according to the KL divergence between the probability distributions of the two particle sets,the number of the particles at current moment is adaptively updated.Selecting the appropriate number of particles while ensuring accuracy greatly reduces the amount of calculation and improves the speed of operation.Through the verification with actually measured flight data,the average number of particles in each sampling period for this algorithm is 40,and the average calculation time in each sampling period is 8ms.Compared with EPF algorithm,this method can significantly reduce the calculation time while ensuring the positioning accuracy,and has certain engineering application value.