基于增量式有限混合模型的多目标状态极大似然估计

闫小喜; 韩崇昭

doi:10.3724/SP.J.1004.2011.00577

基于增量式有限混合模型的多目标状态极大似然估计

doi: 10.3724/SP.J.1004.2011.00577

1.
西安交通大学电子与信息工程学院综合自动化研究所智能网络与网络安全教育部重点实验室、机械制造系统工程国家重点实验室西安 710049

详细信息

通讯作者:
闫小喜

计量
- 文章访问数: 2322
- HTML全文浏览量: 39
- PDF下载量: 1320
- 被引次数: 0
出版历程
- 收稿日期: 2010-08-16
- 修回日期: 2010-12-02
- 刊出日期: 2011-05-20

Maximum Likelihood Estimation of Multiple Target States Based on Incremental Finite Mixture Model

1.
Ministry of Education Key Laboratory for Intelligent Networks and Network Security, State Key Laboratory for Manufacturing Systems Engineering, Institute of Integrated Automation, School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an, 710049

More Information

Corresponding author: YAN Xiao-Xi

摘要

摘要: 提出了增量式有限混合模型来提取概率假设密度滤波器序贯蒙特卡罗实现方式中的多目标状态. 该模型以增量方式构建, 其混合分量采用逐个方式插入其中. 采用极大似然准则来估计多目标状态. 对于给定分量数目的混合模型, 应用期望极大化算法来获得参数的极大似然解. 在新分量插入混合模型时, 保持已有混合模型的参数不变, 仍旧采用极大似然准则从候选新分量集合中选择新插入分量. 新分量插入混合步和期望极大化算法拟合混合参数步交替应用直到混合分量数目达到概率假设密度滤波器的目标数目估计值. 利用k-d树生成插入到混合模型的新分量候选集合. 增量式有限混合模型统一了分量数目变化趋势和粒子集合似然函数的变化趋势, 有助于一步一步地搜寻混合模型的极大似然解. 仿真结果表明, 基于增量式有限混合模型的概率假设密度滤波器状态提取算法在多目标跟踪的应用中优于已有的状态提取算法.
- 多目标状态估计 /
- 增量式有限混合模型 /
- 概率假设密度滤波器 /
- 极大似然 /
- 期望极大化
Abstract: The incremental finite mixture model (IFMM) is proposed to extract target states in the sequential Monte Carlo implementation of probability hypothesis density (PHD) filter. The proposed model is constructed in an incremental way. The mixture components are inserted into mixture model one after another. Maximum likelihood (ML) criterion is adopted in the model for multiple target state estimation. For the mixture model with given component number, expectation maximum (EM) algorithm is applied in obtaining the maximum likelihood solution of model parameters. When the new component is inserted into the mixture model, maximum likelihood criterion is yet adopted for the selection of new component from the candidate set of new components, while the parameters of existing components in mixture model remain invariable. The step of inserting new component into mixture model and the step of maximum likelihood parameter fitting of mixture model by expectation maximum algorithm are alternately applied until the number of mixture components is equal to the estimate of target number produced by the probability hypothesis density filter. The candidate set of new components for inserting into mixture model is generated by k-dimensional tree. The incremental finite mixture model unifies the tendency of component number and that of likelihood of particle set so that it contributes to searching maximum likelihood solution of mixture model step by step. Simulation results show that the state extraction algorithm based on incremental finite mixture model is superior to the existing algorithms for the probability hypothesis density filter in multiple target tracking.
- Multiple target state estimation /
- incremental finite mixture model (IFMM) /
- probability hypothesis density (PHD) filter /
- maximum likelihood (ML) /
- expectation maximum (EM)

HTML全文

参考文献(1)

[1]

Pulford G E. Taxonomy of multiple target tracking methods. IEE Proceedings of Radar, Sonar and Navigation, 2005, 152(5): 291-304 [2] Blackman S, Popoli R. Design and Analysis of Modern Tracking Systems. Boston: Artech House, 1999[3] Bar-Shalom Y, Fortmann T E. Tracking and Data Association. San Diego: Academic Press Professional, 1988[4] Bar-shalom Y, Li X R. Multitarget-Multisensor Tracking: Principles and Techniques. Storrs: YBS Publishing, 1995[5] Daley D J, Vere-Jones D. An Introduction to the Theory of Point Processes (Second Edition). New York: Springer, 2002[6] Mahler R P S. Multitarget Bayes filtering via first-order multitarget moments. IEEE Transactions on Aerospace and Electronic Systems, 2003, 39(4): 1152-1178 [7] Mahler R P S. Statistical Multisource-Multitarget Information Fusion. Norwood: Artech House, 2007[8] Erdinc O, Willett P, Bar-shalom Y. The bin-occupancy filter and its connection to the PHD filters. IEEE Transactions on Signal Processing, 2009, 57(11): 4232-4246 [9] Vo B N, Singh S, Doucet A. Sequential Monte Carlo methods for multitarget filtering with random finite sets. IEEE Transactions on Aerospace and Electronic Systems, 2005, 41(4): 1224-1245 [10] Whiteley N, Singh S, Godsill S. Auxiliary particle implementation of probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 2010, 46(3): 1437-1454 [11] Vo B N, Ma W K. The Gaussian mixture probability hypothesis density filter. IEEE Transactions on Signal Processing, 2006, 54(11): 4091-4104 [12] Pasha S A, Vo B N, Tuan H D, MaW K. A Gaussian mixture PHD filter for jump Markov system models. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 919-936 [13] Clark D E, Bell J. Convergence results for the particle PHD filter. IEEE Transactions on Signal Processing, 2006, 54(7): 2252-2261[14] Clark D E, Vo B N. Convergence analysis of the Gaussian mixture PHD filter. IEEE Transactions on Signal Processing, 2006, 55(4): 1204-1212[15] Mahler R P S. PHD filters of higher order in target number. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1523-1543 [16] Vo B T, Vo B N, Cantoni A. The cardinality balanced multi-target multi-Bernoulli filter and its implementations. IEEE Transactions on Signal Processing, 2009, 57(2): 409-423 [17] Franken D, Schmidt M, Ulmke M. ``Spooky action at a distance'' in the cardinalized probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(4): 1657-1664 [18] Vo B T, Vo B N, Cantoni A. Analytic implementations of the cardinalized probability hypothesis density filter. IEEE Transactions on Signal Processing, 2007, 55(7): 3553-3567 [19] Punithakumar K, Kirubarajan T, Sinha A. Multiple-model probability hypothesis density filter for tracking maneuvering targets. IEEE Transactions on Aerospace and Electronic Systems, 2008, 44(1): 81-98[20] Panta K, Clark D E, Vo B N. Data association and track management for the Gaussian mixture probability hypothesis density filter. IEEE Transactions on Aerospace and Electronic Systems, 2009, 45(3): 1003-1016 [21] Vo B T, Vo B N, Cantoni A. A Bayesian filtering with random finite set observations. IEEE Transactions on Signal Processing, 2008, 56(4): 1313-1326 [22] Rezaeian M, Vo B N. Error bounds for joint detection and estimation of a single object with random finite set observation. IEEE Transactions on Signal Processing, 2010, 58(3): 1493-1506 [23] Wang Y D, Wu J K, Kassim A A, Huang W M. Data-driven probability hypothesis density filter for visual tracking. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(8): 1085-1095 [24] Maggio E, Taj M, Cavallaro A. Efficient multitarget visual tracking using random finite sets. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(8): 1016-1027 [25] Maggio E, Cavallaro A. Learning scene context for multiple object tracking. IEEE Transactions on Image Processing, 2009, 18(8): 1873-1884 [26] Clark D E, Ruiz I T, Petilot Y, Bell J. Particle PHD filter multiple target tracking in sonar images. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 409-416 [27] Clark D E, Ristic B, Vo B N, Vo B T. Bayesian multi-object filtering with amplitude feature likelihood for unknown object SNR. IEEE Transactions on Signal Processing, 2010, 58(1): 26-37 [28] Mahler R. A survey of PHD filter and CPHD filter implementations. In: Proceedings of the Signal Processing, Senor Fusion, and Target Recognition XVI. Orlando, USA: SPIE, 2007. 1-12[29] Mclachlan G J, Peel D. Finite Mixture Models. New York: John Wiley and Sons, 2000[30] Figueiredo M A F, Jain A K. Unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(3): 381-396 [31] Zivkovic Z, Van D H F. Recursive unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 651-656 [32] Clark D E, Bell J. Multi-target state estimation and track continuity for the particle PHD filter. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(4): 1441-1453 [33] Hoffman J R, Mahler R P S. Multitarget miss distance via optimal assignment. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 2004, 34(3): 327-336

施引文献

资源附件(0)

访问统计