| 引用本文: | 陈辉,王莉,韩崇昭.基于随机矩阵建模的Student’s t逆Wishart滤波器[J].控制理论与应用,2022,39(6):1088~1097.[点击复制] |
| CHEN Hui,WANG Li,HAN Chong-zhao.Student’s t inverse Wishart filter based on random matrix modeling[J].Control Theory and Technology,2022,39(6):1088~1097.[点击复制] |
|
| 基于随机矩阵建模的Student’s t逆Wishart滤波器 |
| Student’s t inverse Wishart filter based on random matrix modeling |
| 摘要点击 1313 全文点击 578 投稿时间：2021-11-14 修订日期：2022-05-31 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2022.11108 |
| 2022,39(6):1088-1097 |
| 中文关键词 扩展目标跟踪 随机矩阵模型 逆Wishart分布 异常噪声 |
| 英文关键词 extended target tracking random matrix model inverse Wishart distribution abnormal noise |
| 基金项目 国家自然科学基金项目(62163023, 61873116, 61763029), 甘肃省教育厅产业支撑计划项目(2021CYZC–02), 甘肃省科技计划项目(20JR10RA184) 资助. |
|
| 中文摘要 |
| 针对复杂的异常噪声条件下扩展目标跟踪问题, 本文基于随机矩阵模型(RMM)提出了一种Student’s t逆
Wishart滤波算法. 首先, 运用Student’s t分布对异常的过程噪声和量测噪声进行建模, 运用逆Wishart分布对目标扩展
状态进行建模, 以更加合理的数学模型表征异常噪声条件下基于稀疏量测的目标基本轮廓特征. 进一步的, 本文详
细推导了能稳健估计目标椭圆形状特征的Student’s t逆Wishart滤波算法, 能在形状和方向动态演变过程中有效估计
扩展目标的多重特征. 最后, 通过构造异常噪声条件下椭圆扩展目标跟踪的仿真实验验证了所提算法的有效性. |
| 英文摘要 |
| For the issue of the extended target tracking in the complex abnormal noise conditions, a Student’s t inverse
Wishart filtering algorithm using random matrix model (RMM) is proposed in this article. First, the Student’s t distribution
is used to model the abnormal process noise and the measurement noise, and the inverse Wishart distribution is used to
model the extended state of target. Thus, a more reasonable mathematical model is used to represent the basic contour
features of target based on the sparse measurement with the abnormal noise. Furthermore, this paper deduces a Student’s
inverse Wishart filtering algorithm which can robustly estimate the ellipse shape of target, and can effectively estimate
the multiple characteristics of the extended target with the dynamic evolution of the shape and the direction. Finally, the
simulation experiment about elliptical extended target tracking with the abnormal noise verifies the effectiveness of the
proposed algorithm. |
|
|
|
|
|