基于近邻传播聚类的多扩展目标量测集划分算法

杨金龙; 刘风梅; 王冬; 葛洪伟

doi:10.12000/JR15003

基于近邻传播聚类的多扩展目标量测集划分算法

DOI: 10.12000/JR15003

(江南大学物联网工程学院无锡 214122)

基金项目:

国家自然科学基金(61305017)和江苏省自然科学基金(BK20130154)

详细信息

作者简介:
杨金龙(1981-),男,博士,西安电子科技大学博士毕业,现为江南大学副教授,硕士生导师,主要研究方向为目标跟踪、信号处理、模式识别等.E-mail:jlyang@jiangnan.edu.cn;刘风梅(1987-),女,硕士生,主要研究方向为目标跟踪、信号处理.王冬(1990-),女,硕士生,主要研究方向为目标跟踪、信号处理;葛洪伟(1967-),男,教授,博士生导师,主要研究方向为目标跟踪、模式识别、图像处理等.

通讯作者:
杨金龙jlyang@jiangnan.edu.cn

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出版历程
- 收稿日期: 2015-01-16
- 修回日期: 2015-01-20
- 网络出版日期: 2015-08-28

Affinity Propagation Based Measurement Partition Algorithm for Multiple Extended Target Tracking

(School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

摘要

摘要: 针对杂波环境下多扩展目标量测集难以划分,且时间代价高的问题,该文引入近邻传播聚类技术,提出一种新的多扩展目标量测集划分算法.该算法先采用密度分析技术对量测集进行预处理,滤除部分杂波量测,然后引入近邻传播聚类技术,通过量测间的相互竞争,初步确定扩展目标的数目和质心位置,然后通过扩展目标PHD滤波方法估计目标数目和状态.该方法可有效避免量测集聚类过程中扩展目标质心初始化的干扰,能够准确地划分杂波环境下多扩展目标量测集.与传统的距离划分,K-means++划分方法相比,所提算法能够自适应地确定目标数目,降低时间成本,提高多扩展目标的跟踪性能.
- 目标跟踪 /
- 多扩展目标 /
- 近邻传播聚类 /
- 量测集划分 /
- 距离划分
Abstract: It is difficult to accurately and rapidly partition measurement sets of multiple extended targets in cluttered environment. Hence the affinity propagation method is introduced and a novel measurement partition algorithm is proposed. First, the measurement set is preprocessed by using density analysis to remove clutters from the measurements. Second, the number and location of the extended targets is determined via competition among the measurements. Finally, state estimates are obtained by using the probability hypothesis density filter. Simulations show that the proposed algorithm offers good performance in measurement partitioning of extended target tracking with clutter disturbance. Compared with the distance partition and K-means++ methods, the proposed method effectively minimizes the computation time and retrieves the number of targets iteratively.
- Target tracking /
- Multiple extended target /
- Affinity propagation /
- Measurement partition /
- Distance partition

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参考文献(17)

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