有偏量测下基于最大相关熵卡尔曼滤波的目标跟踪方法  

A target tracking method based on maximum correntropy Kalman filtering under biased measurements

作　　者：韦春玲 余润华 吴孙勇[2,3] 李明 Wei Chunling;Yu Runhua;Wu Sunyong;Li Ming(School of Information and Communication Engineering,Guilin University of Electronic Technology,Guilin 541004,China;School of Mathematics and Computational Science,Guilin University of Electronic Technology,Guilin 541004,China;Guangxi Key Laboratory of Cryptography and Information Security,Guilin 541004,China;Guangxi Zhuang Autonomous Region Engineering Research Center for Intelligent Electromagnetic Spectrum Perception and Control Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学信息与通信学院,广西桂林541004 [2]桂林电子科技大学数学与计算科学学院,广西桂林541004 [3]广西密码学与信息安全重点实验室,广西桂林541004 [4]广西壮族自治区智能电磁频谱感知与控制技术工程研究中心,广西桂林541004

出　　处：《电子技术应用》2024年第7期7-13,共7页Application of Electronic Technique

基　　金：国家自然科学基金(62263007);中央引导地方科技发展资金项目(桂科ZY22096026);广西重点研发项目(桂科AB23026147,桂科AB21196041);广西高校数据分析与计算重点实验室开放基金;广西应用数学中心(桂林电子科技大学)开放基金;“认知无线电与信息处理”教育部重点实验室2021年开放基金项目(CRKL210101)。

摘　　要：针对传感器存在系统偏差且噪声非高斯条件下目标状态估计精度较差的问题,提出一种有偏量测下基于最大相关熵卡尔曼滤波(Maximum Correntropy Kalman Filter,MCKF)的目标跟踪方法。该方法通过引入差分机制,利用目标相邻时刻的有偏量测之差构建差分量测方程,有效克服了系统偏差的影响。随后基于最大相关熵准则(Maximum Correntropy Criterion,MCC)量化估计误差的高阶矩信息,并以差分量测为先验条件推导出有偏量测下算法的滤波迭代方程。仿真结果表明,当系统观测值受传感器系统偏差和非高斯噪声干扰时,与现有方法相比,所提方法具有更好的跟踪性能。To address the issue of poor target state estimation accuracy under conditions where sensors have system bias and noise is non-Gaussian,a target tracking method based on the maximum correntropy Kalman filter(MCKF)under biased measurements is proposed.This approach introduces a differential mechanism that constructs differential measurement equations from the biased measurements of the target at adjacent time points,effectively mitigating the effects of system bias.Subsequently,the maximum correntropy criterion(MCC)is employed to quantify the higher-order moment information of the estimation error with differential measurements serving as a priori conditions.This leads to the derivation of the filtering iterative equations for the algorithm under biased measurements.Simulation results demonstrate that,when system observations are affected by sensor system bias and non-Gaussian noise,the proposed method outperforms existing approaches in terms of tracking performance.

关 键 词：系统偏差 非高斯噪声 最大相关熵准则 量测差分 卡尔曼滤波 

分 类 号：TN953[电子电信—信号与信息处理]