高斯噪声下线性和非线性运动轨迹的滤波算法对比研究  

Comparison research on filtering algorithms for linear andnonlinear motion trajectories in Gaussian noise

作　　者：朱春华[1] 石震 ZHU Chunhua;SHI Zhen(College of Information Science and Engineering,Henan University of Technology,Zhengzhou 450001,China)

机构地区：[1]河南工业大学信息科学与工程学院,河南郑州450001

出　　处：《现代电子技术》2021年第9期43-47,共5页Modern Electronics Technique

基　　金：国家自然科学基金项目资助:基于CSI的无线层析成像探测仓储粮食异常粮情的研究(61871176);河南省高等学校重点科研项目应用研究计划(19A510011);河南工业大学科学研究基金(省属高校基本科研业务费专项资金)自然科学领域项目(2018RCJH18)。

摘　　要：在目标跟踪系统中,目标的运动轨迹定位精度受到噪声的影响。为了提高定位精度,可采用卡尔曼滤波(KF)、扩展卡尔曼滤波(EKF)、无迹卡尔曼滤波(UKF)和Savitzky⁃Golay(SG)滤波方法对目标运动轨迹进行滤波平滑处理,进而为后续的采样、分析奠定基础。通过理论分析和仿真对比分析了线性和非线性运动轨迹的滤波性能,结果表明所提的四种滤波方法均能够改善目标定位精度,且SG滤波在最佳窗宽下的滤波性能最优。The target tracking system′s the positioning accuracy of the target motion trajectory is affected by the noise.Therefore,the Kalman filtering(KF),extended Kalman filtering(EKF),unscented Kalman filtering(UKF)and Savitzky⁃Golay(SG)filtering methods are used for smoothing processing of the target motion trajectory to improve the positioning accuracy,which can lay a foundation for subsequent sampling and analysis.In this paper,the filtering performance of the four methods for linear or nonlinear motion trajectories is analyzed by means of theoretical analysis and simulation comparison.The results show that all of the four filtering methods proposed can improve the accuracy of target positioning.Besides,SG filtering has the optimal filtering performance at the best window width.

关 键 词：目标跟踪 运动轨迹定位 滤波平滑处理 理论分析 仿真对比 滤波性能分析 定位精度 

分 类 号：TN911.1-34[电子电信—通信与信息系统]