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。
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.
作者
陈辉
张丁丁
连峰
韩崇昭
CHEN Hui;ZHANG Dingding;LIAN Feng;HAN Chongzhao(School of electrical engineering and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;School of Automation Science and Engineering,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《电子与信息学报》
EI
CAS
CSCD
北大核心
2024年第8期3353-3362,共10页
Journal of Electronics & Information Technology
基金
国家自然科学基金(62163023,62366031,62363023,61873116)
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(本基金无项目编号)
2024年甘肃省重点人才项目(本基金无项目编号)。
