扩展目标跟踪Student's t逆Wishart平滑算法
Student's t Inverse Wishart Smoothing Algorithm for Extended Target Tracking
陈辉 1张丁丁 1连峰 2韩崇昭2
作者信息
- 1. 兰州理工大学电气工程与信息工程学院 兰州 730050
- 2. 西安交通大学自动化科学与工程学院 西安 710049
- 折叠
摘要
脉冲干扰和离群量测信息等因素通常会导致异常的厚尾噪声,这使得以高斯假设为前提的扩展目标跟踪(ETT)估计器的性能急剧降低,针对该问题该文提出一种基于扩展目标随机矩阵模型(RMM)的Student's t逆Wishart平滑(StIWS)算法.首先,将目标的运动状态以及过程噪声和量测噪声建模为Student's t分布以表征异常噪声对扩展目标概率分布的影响,将目标扩展状态建模为服从逆Wishart分布的随机矩阵.然后,在Student's t贝叶斯平滑框架下,详细推导了能在扩展目标的多重特征动态演变的过程中有效估计目标状态的StIWS算法.最后,通过扩展目标跟踪的仿真实验结果和真实场景实验结果验证了所提算法的有效性.
Abstract
Elements such as pulse interference and outlier measurement information usually lead to abnormal heavy-tailed noise,which sharply reduces the performance of the Extended Target Tracking(ETT)estimator based on the Gaussian hypothesis.To address this problem,a Student's t Inverse Wishart Smoothing(StIWS)algorithm based on the Random Matrix Model(RMM)is proposed.Firstly,the kinematic state of the target,process noise and measurement noise are modeled as a Student's t distribution to characterize the effect of anomalous noise on the probability distribution of extended target,and the extended state of target is modeled as a random matrix which obeys inverse Wishart distribution.Then,in a Student's t bayesian smoothing frame,the StIWS algorithm is derived in detail,which can effectively estimate target state in the process of the dynamic evolution of multiple characteristics of extended target.Finally,the effectiveness of the proposed algorithm is verified by the simulation experiment and the engineering experiment of extended target tracking.
关键词
扩展目标跟踪/Student's/t平滑/逆Wishart分布/厚尾噪声Key words
Extended target tracking/Student's t smoothing/Inverse wishart distribution/Heavy-tailed noise引用本文复制引用
基金项目
国家自然科学基金(62163023)
国家自然科学基金(62366031)
国家自然科学基金(62363023)
国家自然科学基金(61873116)
甘肃省教育厅产业支撑计划项目(2021CYZC-02)
2023年甘肃省军民融合发展专项资金项目()
2024年甘肃省重点人才项目()
出版年
2024
NETL
NSTL
万方数据