非平稳异常噪声条件下的扩展目标跟踪方法  

Extended Target Tracking Method under Non-stationary Abnormal Noise Conditions

作　　者：陈辉[1] 张欣雨 连峰[2] 韩崇昭[2] 张光华[2] CHEN Hui;ZHANG Xinyu;LIAN Feng;HAN Chongzhao;ZHANG Guanghua(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)

机构地区：[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

出　　处：《电子与信息学报》2025年第3期803-813,共11页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(62163023,61873116,62366031,62363023);甘肃省基础研究创新群体(25JRRA058);中央引导地方科技发展资金项目(25ZYJA040);甘肃省重点人才项目(2024RCXM86),甘肃省军民融合发展专项资金。

摘　　要：针对非平稳异常噪声环境下扩展目标跟踪问题,该文提出一种基于高斯-学生t混合(GSTM)扩展目标跟踪方法。首先,将过程噪声和量测噪声建模为GSTM分布,以表征非平稳厚尾噪声,并通过引入伯努利随机变量,将目标的运动状态和量测似然函数建模为分层高斯形式。其次,在随机矩阵(RMM)滤波框架下,使用变分贝叶斯方法详细推导了非平稳厚尾噪声下的GSTM扩展目标跟踪算法。该算法通过建模高斯噪声与厚尾噪声之间的非平稳过程,精确表征噪声特性,从而在非平稳异常噪声环境下稳健捕捉扩展目标的质心位置和轮廓形态。最后,构建非平稳异常噪声环境下的扩展目标跟踪仿真实验,并通过高斯-瓦瑟斯坦距离对实验结果进行效果评估,验证了所提出算法的合理性。此外,真实场景实验结果进一步证明了该算法在实际应用中的有效性和鲁棒性。Objective This paper addresses the problem of extended target tracking in the presence of non-stationary abnormal noise.Traditional Gaussian extended target filters and Student's t filters rely on the assumption of stationary noise distributions,which limits their performance in environments with non-stationary abnormal noise.Non-stationary noise,common in practical applications,is especially prevalent in complex environments where the noise frequently shifts between Gaussian and heavy-tailed distributions.To overcome this challenge,a Gaussian-Student's t Mixture(GSTM)distribution is proposed for modeling non-stationary abnormal noise in extended target tracking.The GSTM distribution is used to model the noise accurately,and a filter is developed to track the target's kinematic state and shape effectively under non-stationary measurement and process noise conditions.This method is shown to be robust in complex environments,offering enhanced accuracy,robustness,and applicability for extended target tracking.Methods The GSTM distribution is employed to model both process and measurement noise,enabling dynamic adjustment of mixture parameters to capture the evolving characteristics of noise distributions in nonstationary environments.To optimize computation,Bernoulli random variables are introduced,and the target's one-step prediction and measurement likelihood functions are reformulated as a hierarchical Gaussian model based on the GSTM distribution.This approach facilitates adaptive switching between Gaussian and Student's t distributions,streamlining the inference process and simplifying posterior computation,which reduces the complexity of parameter estimation.Within the Random Matrix Model(RMM)framework,Variational Bayesian(VB)inference is applied to jointly estimate the target's kinematic state,extension state,mixture parameters,and noise characteristics.During the filtering update phase,a dynamic adjustment mechanism is introduced for the one-step prediction error covariance matrix and observation noise covar

关 键 词：扩展目标跟踪 随机矩阵 高斯-学生t混合分布 变分贝叶斯方法 

分 类 号：TN911.7[电子电信—通信与信息系统] TP274[电子电信—信息与通信工程]