A Partition Algorithm of Measurement Sets for Extended Objects

doi:10.7652/xjtuxb201409004

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A Partition Algorithm of Measurement Sets for Extended Objects

更新时间：2025-07-09

- A Partition Algorithm of Measurement Sets for Extended Objects
- Vol. 48, Issue 9, Pages: 19-23+29(2014)
- 作者机构：
  
  江苏大学电气信息工程学院,江苏,镇江,212013
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201409004
  CLC： TP274
- Online First：10 September 2014，
  
  Published：2014
- 稿件说明：
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A Partition Algorithm of Measurement Sets for Extended Objects[J]. 2014, 48(9): 19-23+29.
DOI：

A Partition Algorithm of Measurement Sets for Extended Objects[J]. 2014, 48(9): 19-23+29. DOI： 10.7652/xjtuxb201409004.

摘要

针对拓展目标概率假设密度滤波器采用的量测集合所有可能分割方式在实际中几乎不能够实现的问题

提出了一种采用有限混合模型的量测集合近似分割算法

对所有可能分割方式进行近似处理。算法利用有限混合模型拟合量测集合以实现对量测集合的分割

首先利用期望极大化算法极大似然估计混合参数

然后利用量测来源的条件概率分割量测集合

最后以二维场景为例进行了仿真实验。仿真结果表明:新算法在所有时刻上的最优子模式分配和混合分量数目均小于现有的典型量测集合分割算法; 在拓展目标跟踪性能上

新算法具有更好的多拓展目标跟踪性能。

Abstract

An approximate partition algorithm of measurement sets is proposed to overcome the problem that it is impossible to implement all the possible partitions of a measurement set in density filters with extended object probability hypothesis

and the algorithm bases on a finite mixture model. The finite mixture model is used to fit the measurement set and then the partition of the measurement set is implemented. The expectation maximization algorithm is employed to obtain the maximum likelihood estimation of mixture parameters. Then

the conditional probability of the measurement source is applied in partitioning the measurement set. Simulation results show that the proposed algorithm is superior to typical partition algorithm of measurement sets in extended object tracking.

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references

PULFORD G E. Taxonomy of multiple target tracking methods[J]. IET Proceeding of Radar, Sonar and Navigation, 2005, 152(2): 291-304.

BLACKMAN S, POPOLI R. Design and analysis of modern tracking systems[M]. Norwood, USA: Artech House, 1999.

BAR S Y, LI Xiaorong. Multitarget-multisensor tracking: principles and techniques[M]. Storrs, USA: YBS Publishing, 1995.

MAHLER R. Multi-target Bayes filtering via first-order multi-target moments[J]. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178.

MAHLER R. PHD filters of higher order in target number[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543.

MAHLER R. Statistical multisource-multitarget information fusion[M]. Norwood, USA: Artech House, 2007.

MAHLER R, VO B T, VO B N. Forward-backward probability hypothesis density smoothing[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(1): 707-728.

VO B N, VO B T, MAHLER R. Closed-form solutions to forward-backward smoothing[J]. IEEE Transactions on Signal Processing, 2012, 60(1): 2-17.

闫小喜, 韩崇昭. 应用Dirichlet分布的概率假设密度多目标跟踪[J]. 西安交通大学学报, 2011, 45(2): 6-10.

YAN Xiaoxi, HAN Chongzhao. Multiple target tracking by probability hypothesis density based on Dirichlet distribution[J]. Journal of Xi'an Jiaotong University, 2011, 45(2): 6-10.

VO B N, SINGH S, DOUCET A. Sequential Monte Carlo methods for multi-target filtering with random finite sets[J]. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245.

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

VO B T, VO B N, CANTONI A. Analytic implementations of the cardinalized probability hypothesis density filter[J]. IEEE Transactions on Signal Processing, 2007, 55(7): 3553-3567.

GILHOLM K, SALMOND D. Spatial distribution for tracking extended objects[J]. IEEE Proceedings of Radar, Sonar and Navigation, 2005, 152(5): 364-371.

VERMAAK J, IKOMA N, GODSILL S J. Sequential Monte Carlo framework for extended object tracking[J]. IEEE Proceedings of Radar, Sonar and Navigation, 2005, 152(3): 353-363.

ANGELOVA D, MIHAYLOYA L. Extended object tracking using Monte Carlo methods[J]. IEEE Transactions on Signal Processing, 2008, 56(2): 825-832.

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

VO B T, VO B N, CANTONI A. Bayesian filtering with random finite set observations[J]. IEEE Transactions on Signal Processing, 2008, 56(4): 1313-1326.

MAHLER R. PHD filters for nonstandard targets: I extended object[C]∥The 12th International Conference on Information Fusion. Piscataway, NJ, USA: IEEE, 2009: 915-921.

GRANSTROM K, LUNDQUIST C, ORGUNER O. Extended target tracking using a Gaussian-mixture PHD filter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(4): 3268-3286.

FIGUEIREDO M, JAIN A K. Unsupervised learning of finite mixture models[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2002, 24(3): 381-396.

ZIVKOVIC Z, HEIJDEN F V. Recursive unsupervised learning of finite mixture models[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2004, 26(5): 651-656.

HOFFMAN J, MAHLER R. Multitarget miss distance via optimal assignment[J]. IEEE Transactions on Systems, Man and Cybernetics: Part A, 2004, 34(3): 327-336.

SCHUHMACHER D, VO B T, VO B N. A consistent metric for performance evaluation of multi-object filters[J]. IEEE Transactions on Signal Processing, 2008, 56(8): 3447-3457.

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