基于高斯混合概率假设密度的运动参数估计组合平滑滤波算法  

Motion Parameter Estimation Combined Smoothing Filter Algorithm Based on Gaussian Mixture Probability Hypothesis Density

作　　者：黄庆东 李晓瑞 曹艺苑 刘青[2] HUANG Qingdong;LI Xiaorui;CAO Yiyuan;LIU Qing(School of Communication and Information Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China;School of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China)

机构地区：[1]西安邮电大学通信与信息工程学院,西安710121 [2]西安理工大学自动化与信息工程学院,西安710048

出　　处：《电子与信息学报》2022年第7期2488-2495,共8页Journal of Electronics & Information Technology

基　　金：国防科研试验信息安全实验室基础研究项目(2018XXAQ09)。

摘　　要：针对高斯混合概率假设密度(GM-PHD)滤波器在目标速度未知或不准确时,目标状态估计性能较差,该文提出一种基于GM-PHD的运动参数估计组合平滑滤波算法。该算法通过目标状态提取速度信息,经过中值平滑和线性平滑组合处理提升速度估计准确性,然后将速度反馈给GM-PHD滤波器的状态转移方程,提高状态预测精度。仿真结果表明,目标速度未知或不准确时,所提算法能够明显改善GM-PHD滤波器状态估计性能。Considering poor performance of target state estimation for Gaussian Mixture Probability Hypothesis Density(GM-PHD)filter when the target velocity is unknown or inaccurate,a combined smoothing filtering algorithm for motion parameter estimation based on GM-PHD is proposed.The velocity information is extracted from the target state,and the accuracy of velocity estimation is improved through the combined processing of median smoothing and linear smoothing.Then,the velocity is fed back to the state transition equation of the GM-PHD filter to improve the accuracy of state prediction.Simulation results show that the proposed algorithm can significantly improve the state estimation performance of GM-PHD filter when the target velocity is unknown or inaccurate.

关 键 词：目标跟踪 高斯混合概率假设密度滤波 参数估计 组合平滑 

分 类 号：TN911.73[电子电信—通信与信息系统]