论文:2018,Vol:36,Issue(3):420-425
引用本文:
程轩, 宋骊平, 姬红兵. 基于mean shift和图结构的GMPHD扩展目标跟踪[J]. 西北工业大学学报
Cheng Xuan, Song Liping, Ji Hongbing. Extended Target GMPHD Filter Based on Mean Shift and Graph Structure[J]. Northwestern polytechnical university

基于mean shift和图结构的GMPHD扩展目标跟踪
程轩, 宋骊平, 姬红兵
西安电子科技大学 电子工程学院, 陕西 西安 710071
摘要:
针对当前扩展目标跟踪算法中,量测划分数过多、计算量过大,目标交叉时刻易产生漏估等问题,提出一种基于mean shift和图结构的GMPHD扩展目标跟踪算法。首先,引入核密度估计剔除杂波量测;其次,采用mean shift算法对扩展目标量测集进行划分,并依据图结构更新后反馈回的信息判断是否需要进行子划分;然后,采用扩展目标GMPHD算法进行滤波处理;最后,对滤波结果进行一步预测,更新图结构,并使用更新后的图结构信息指导下一时刻的量测划分。matlab仿真表明,所提算法大幅减少了量测划分数,降低了运算量,解决了扩展目标交叉时刻的漏估问题。
关键词:    扩展目标跟踪    mean shift    图结构    GMPHD    量测划分    计算效率    matlab   
Extended Target GMPHD Filter Based on Mean Shift and Graph Structure
Cheng Xuan, Song Liping, Ji Hongbing
School of Electronic Engineering, Xidian University, Xi'an 710071, China
Abstract:
In view of excessive measurements partition number, a large computation load of extended target tracking and leakage estimation when the extended targets cross, an extended target tracking algorithm based on GMPHD with mean shift and graph structure is proposed. Firstly, the kernel density estimation is used to eliminate the clutter measurements. Secondly, mean shift algorithm is adopted to divide the extended target measurements set, and sub-division is considered to carry or not based on the information fed back from the updated graph structure. Then, the extended target GMPHD algorithm is used to filter. Finally, the graph structure is updated by the one-step predicted value of the filtering result, and the updated graph structure information is used to guide the measurement partition at the next moment. Matlab simulation shows that the algorithm proposed decreases largely the number of measurements partition, reduces the computational complexity, and solves the leakage estimation problem when the targets cross.
Key words:    extended target tracking    mean shift    graph structure    GMPHD    measurements partition    computational efficiency    matlab   
收稿日期: 2017-04-08     修回日期:
DOI:
基金项目: 国家自然科学基金(61372003)资助
通讯作者:     Email:
作者简介: 程轩(1991-),西安电子科技大学硕士研究生,主要从事目标跟踪算法研究。
相关功能
PDF(1262KB) Free
打印本文
把本文推荐给朋友
作者相关文章
程轩  在本刊中的所有文章
宋骊平  在本刊中的所有文章
姬红兵  在本刊中的所有文章

参考文献:
[1] Karl G, Umut O, Ronald M, Chriustian L. Corrections on:Extended Target Tracking Using a Gaussian-Mixture PHD Filter[J]. IEEE Trans on Aerospace and Electronic Systems, 2017, 53(2):1055-1058
[2] Gemine V, Paolo B, Karl G, Peter W. Multistatic Bayesian Extended Target Tracking[J]. IEEE Trans on Aerospace and Electronic Systems, 2016, 52(6):2626-2643
[3] Mahler R. PHD Filters for Nonstandard Targets, I:Extended Targets[C]//Proceedings of the 12th International Conference on Information Fusion, 2009:915-921
[4] Granstrom K, Lundquist C, Orguner U. A Gaussian Mixture PHD Filter for Extended Target Tracking[C]//Proceedings of International Conference on Information Fusion, 2010:915-921
[5] Granstrom K, Lundquist C, Orguner U. Extended Target Tracking Using a Gaussian-Mixture PHD Filter[J]. IEEE Trans on Aerospace and Electronic Systems, 2012, 48(4):3268-3286
[6] Zhang Y Q, Ji H B. A Novel Fast Partitioning Algorithm for Extended Target Tracking Using a Gaussian Mixture PHD Filter[J]. Signal Processing, 2013, 93(11):2975-2985
[7] 孔云波, 冯新喜, 危璋. 利用高斯混合概率假设密度滤波器对扩展目标量测集进行划分[J]. 西安交通大学学报, 2015, 49(7):126-133 Kong Yunbo, Feng Xinxi, Wei Zhang. A Measurement Set Partitioning for Extended Target Tracking Using a Gaussian Mixture Extended-Target Gaussian Mixture Probability Hypothesis Density Filter[J]. Journal of Xi'an Jiaotong University, 2015, 49(7):126-133(in Chinese)
[8] 刘风梅, 葛洪伟, 杨金龙, 李鹏. 基于均值漂移聚类的扩展目标量测集划分算法[J]. 计算机工程, 2014, 40(12):182-187 Liu Fengmei, Ge Hongwei, Yang Jinglong, Li Peng. Extended Target Measurement Set Partition Algorithm Based on Mean Shift Clustering[J]. Computer Engineering, 2014, 40(12):182-187(in Chinese)
[9] Cheng Y. Mean Shift, Mode Seeking, and Clustering[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1995, 17(8):790-799
[10] 李翠芸, 桂阳, 刘靳. 基于均值漂移迭代的新生未知多扩展目标跟踪[J]. 控制与决策, 2017, 32(3):521-525 Li Cuiyun, Gui Yang, Liu Jin. Unknown Newly Born Multiple Extended Targets Tracking Based on Mean Shift Iteration[J]. Control and Decision, 2017, 32(3):521-525(in Chinese)
[11] Schuhmacher D, Vo B T, Vo B N. A Consistent Metric for Performance Evaluation of Multi-Object Filters[J]. IEEE Trans on Signal Processing, 2008, 56(8):3447-3457
相关文献:
1.任超, 李洪双.基于失效概率的全局重要性测度分析的交叉熵方法[J]. 西北工业大学学报, 2017,35(3): 536-544