基于最小模糊误差熵的机动目标跟踪新方法

A New Method of Maneuvering Target Tracking Based on Minimum Fuzzy Error Entropy

为了提高不同噪声影响下机动目标跟踪的性能,提出了一种基于最小模糊误差熵无迹滤波(Minimum Fuzzy Error Entropy Unscented Filter,MFEE-UF)机动目标跟踪新方法.在提出方法中,通过引入模糊隶属度表示不同误差样本对估计结果的不同影响,构建最小模糊误差熵准则(Minimum Fuzzy Error Entropy Criterion,MFEEC),解决了普通误差熵中的权重单一化问题,并利用该准则优化无迹滤波;在推导MFEE-UF过程中,首先利用无迹变换(Unscented Transformation,UT)框架得到先验状态估计和先验协方差估计,并通过系统重建得到误差信息,再根据MFEEC构建目标函数,最后利用定点迭代法递归求得后验状态估计结果和后验协方差估计.此外,本文采用一种自适应的核宽设置方法.实验结果表明,该算法能够具有良好的目标跟踪效果,且表现出较强的稳定性.

In order to improve the accuracy of target tracking results in nonlinear systems under different kinds of noise,minimum fuzzy error entropy unscented filter(MFEE-UF)is proposed in this paper.In this proposed method,the fuzzy membership is introduced to represent the different effects of different error samples on the estimation results,solving the problem of same weight in common error entropy.And then the minimum fuzzy error entropy criterion(MFEEC)is constructed and used to optimize the unscented filtering,deriving MFEE-UF.In this proposed algorithm,the unscented transformation(UT)framework is used to obtain a priori state estimation and a priori covariance estimation,and error information is obtained by system reconstruction.Then the objective function is constructed based on MFEEC,and finally the posterior state estimation and the posterior covariance estimation is solved by using fixed-point iteration method.In addition,kernel width is set adaptively.Simulations show that the proposed algorithm has strong stability,and can track a target more accurately.