| 引用本文: | 占荣辉,王丽萍.非椭圆扩展目标联合跟踪与分类算法.[J].国防科技大学学报,2022,44(5):158-170.[点击复制] |
| ZHAN Ronghui,WANG Liping.Joint tracking and classification algorithm of non-ellipsoidal extended target[J].Journal of National University of Defense Technology,2022,44(5):158-170[点击复制] |
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| 非椭圆扩展目标联合跟踪与分类算法 |
| 占荣辉1,王丽萍1,2 |
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(1. 国防科技大学 电子科学学院, 湖南 长沙 410073;2. 中国人民公安大学 侦查学院, 北京 100038)
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| 摘要: |
| 充分利用目标尺寸和形状信息,提出了一种基于星凸随机超曲面模型(random hypersurface model, RHM)的非椭圆扩展目标联合跟踪与分类(joint tracking and classification, JTC)算法。将目标空间扩展状态建模为星凸形状,通过目标类别相关先验信息的矢量化建模,建立起其与目标瞬时扩展状态的关系,并在统一的贝叶斯滤波框架下,实现跟踪与分类的一体化处理；进一步对目标运动学状态和扩展状态单独进行建模,并通过构建扩展状态的似然函数,利用粒子滤波实现目标类别概率算式的递推处理。仿真结果表明:与基于椭圆形状的扩展目标JTC算法相比,所提算法能对尺寸相近、形状不同的目标进行准确分类,同时可改善目标状态的估计效果；与基于星凸RHM的扩展目标跟踪算法相比,所提算法能大幅提高目标状态的估计性能。 |
| 关键词: 联合跟踪与分类 扩展目标 星凸随机超曲面 扩展状态 |
| DOI：10.11887/j.cn.202205017 |
| 投稿日期：2022-01-06 |
| 基金项目:国家自然科学基金资助项目(62271491,61471370) |
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| Joint tracking and classification algorithm of non-ellipsoidal extended target |
| ZHAN Ronghui1, WANG Liping1,2 |
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(1. College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China;2. School of Criminal Investigation, People′s Public Security University of China, Beijing 100038, China)
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| Abstract: |
| By making full use of the target size and shape information, a NEET (non-ellipsoidal extended target) JTC (joint tracking and classification) algorithm was proposed on the basis of the star-convex RHM (random hypersurface model). In the proposed algorithm, the target extent state was modeled as star-convex shape. By modeling the target class-related prior information with vector form, constructing its relationship with the simultaneous extent state, and integrating it into the framework of Bayesian filter, the joint processing of tracking and classification was realized. Additionally, two separate vectors were used to model the target state, and the probability update of target class was realized by particle filter based on likelihood function derivation. The simulation results show that the NEET JTC algorithm can accurately classify targets with similar size but different shapes, and improve the target state estimation results when compared with the extended target JTC algorithm based on elliptical shape. The results also show that the proposed algorithm can significantly improve the target state estimation performance when compared with the extended target tracking algorithm based on star-convex RHM. |
| Keywords: joint tracking and classification extended target star-convex random hypersurface extent state |
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