多扩展目标的高斯混合概率假设密度滤波器

韩玉兰 1; 朱洪艳 1; 韩崇昭 1; 王静 2

doi:10.7652/xjtuxb201404017

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多扩展目标的高斯混合概率假设密度滤波器

更新时间：2025-04-29

- 多扩展目标的高斯混合概率假设密度滤波器
- Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets
- 西安交通大学学报 2014年48卷第4期页码：95-101
- 作者机构：
  
  西安交通大学电子与信息工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2013-09-27。作者简介: 韩玉兰(1982—),女,博士生;朱洪艳(通信作者),女,副教授。基金项目: 国家自然科学基金资助项目(61203220,61221063,61074176);国家“973计划”资助项目(2013CB329405)
- DOI：10.7652/xjtuxb201404017
  中图分类号： TN274
- 网络首发：2014-04-10，
  
  纸质出版：2014
- 稿件说明：
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韩玉兰 1, 朱洪艳 1, 韩崇昭 1, 等. 多扩展目标的高斯混合概率假设密度滤波器[J]. 西安交通大学学报, 2014,48(4):95-101.

Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets[J]. 2014, 48(4): 95-101.
韩玉兰 1, 朱洪艳 1, 韩崇昭 1, 等. 多扩展目标的高斯混合概率假设密度滤波器[J]. 西安交通大学学报, 2014,48(4):95-101. DOI： 10.7652/xjtuxb201404017.

Gaussian-Mixture Probability Hypothesis Density Filter for Multiple Extended Targets[J]. 2014, 48(4): 95-101. DOI： 10.7652/xjtuxb201404017.

摘要

针对多扩展目标跟踪中状态信息难以估计的问题

提出了一种可以估计扩展目标运动状态和形状信息的多扩展目标高斯混合概率假设密度(RHM-GMPHD)滤波器。首先利用描述凸星形扩展目标量测源分布的随机超曲面模型和传感器量测方程

建立扩展目标运动状态及形状信息与量测之间关系的伪量测函数; 然后结合扩展目标状态预报信息

推导了扩展目标状态更新方程

递推地对扩展目标运动状态及形状信息进行估计跟踪。此外

还建立了Jaccard距离来度量RHM-GMPHD滤波器对目标形状的估计性能。与联合概率数据关联(JPDA)滤波器和GMPHD滤波器相比

RHM-GMPHD滤波器不仅可以估计凸星形扩展目标的形状信息

并能有效提高对目标数和运动状态的估计精度。仿真实验表明

RHM-GMPHD滤波器对质心估计的均方根误差分别约为JPDA和GMPHD滤波器的1/3和1/2

对目标数的估计接近真实值

对形状估计的Jaccard距离一般小于0.2。

Abstract

A multiple extended-target Gaussian-mixture probability hypothesis density(RHM-GMPHD)filter

which provides the kinematic state and the extension state of extended targets

is proposed to address the difficultly estimated extension state. The pseudo-measurement likelihood function describing the relationship between kinematic state and extension state of extended target and measurements is constructed via the random hypersurface model(RHM)for convex-star extended target and sensor measurement function. Then the predicted state is considered

the update of extend target filter is derived to recursively estimate the kinematic state and extension state for extended targets. Moreover

the Jaccard distance is presented to evaluate the performance of the estimate extension state. Compared with the joint probabilistic data association(JPDA)and GMPHD filter

RHM-GMPHD provides the extension state and enhances the precision of the estimate number and the estimate kinematic state. Simulations indicate that the root-mean-square error of centroid from RHM-GMPHD gets 1/3 of that from JPDA or 1/2 of that from GMPHD. The estimation number of extended targets approaches the true value

and Jaccard distance gets usually less than 0.2.

关键词

Keywords

references

KOCH J W. Bayesian approach to extended object and cluster tracking using random matrices[J]. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(3): 1042-1059.

连峰, 韩崇昭, 刘伟峰, 等. 基于 SMC-PHDF 的部分可分辨的群目标跟踪算法[J]. 自动化学报, 2010, 36(5): 731-741.

LIAN Feng, HAN Chongzhao, LIU Weifeng, et al. Tracking partly resolvable group targets using SMC-PHDF[J]. Acta Automatica Sinica, 2010, 36(5): 731-741.

FELDMANN M, FRANKEN D, KOCH J W. Tracking of extended objects and group targets using random matrices[J]. IEEE Transactions on Signal Processing, 2011, 59(4): 1409-1420.

BAUM M, HANEBECK U D. Random hypersurface models for extended object tracking[C]∥ Proceedings of International Symposium on Signal Processing and Information Technology. Piscataway, NJ, USA: IEEE, 2009: 178-183.

BAUM M, HANEBECK U D. Shape tracking of extended objects and group targets with star-convex RHMs[C]∥ Proceedings of the International Conference on Information Fusion. Piscataway, NJ, USA: IEEE, 2011: 338-345.

BAUM M, NOACK B, HANEBECK U D. Mixture random hypersurface models for tracking multiple extended objects[C]∥Proceedings of the IEEE Conference on Decision and Control. Piscataway, NJ, USA: IEEE, 2011: 3166-3171.

WIENEKE W, KOCH J W. Probabilistic tracking of multiple extended targets using random matrices[C]∥Proceedings of Signal and Data Processing of Small Targets. Orlando, Florida, USA: SPIE, 2010: 419-430.

MAHLER R. PHD filters for nonstandard targets: I Extended targets[C]∥Proceedings of the International Conference on Information Fusion. Piscataway, NJ, USA: IEEE, 2009: 915-921.

ORGUNER U, LUNDQUIST C, GRANSTROM K. Extended target tracking with a cardinalized probability hypothesis density filter[C]∥Proceedings of the 14th International Conference on Information Fusion. Piscataway, NJ, USA: IEEE, 2011: 1-8.

GRANSTROM K, LUNDQUIST C, ORGUNER U. Extended target tracking using a Gaussian mixture PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3268-3286.[11] GRANSTROM K, ORGUNER U. A PHD filter for tracking multiple extended targets using random matrices[J]. IEEE Transactions on Signal Processing, 2012, 60(11): 5657-5671.

ZHANG D S, LU G J. Study and evaluation of different Fourier methods for image retrieval[J]. Image and Vision Computing, 2005, 23(1): 33-49.

VO B N, MA W K. The Gaussian mixture probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104.

潘磊, 雷钰丽, 王崇骏, 等. 基于权重的Jaccard相似度度量的实体识别方法[J]. 北京交通大学学报, 2009, 33(6): 141-145.

PAN Lei, LEI Yuli, WANG Chongjun, et al. Method on entity identification using similarity measure based on weight of Jaccard[J]. Journal of Beijing Jiaotong University, 2009, 33(6): 141-145.

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