论文:2015,Vol:33,Issue(3):506-511
引用本文:
马天力, 王新民, 曹宇燕, 黄誉, 穆凌霞. 基于数据流聚类的多目标跟踪算法[J]. 西北工业大学学报
Ma Tianli, Wang Xinmin, Cao Yuyan, Huang Yu, Mu Lingxia. An Algorithm of Multi-Target Tracking Based on Clustering Data Stream[J]. Northwestern polytechnical university

基于数据流聚类的多目标跟踪算法
马天力, 王新民, 曹宇燕, 黄誉, 穆凌霞
西北工业大学, 自动化学院, 陕西 西安 710072
摘要:
针对传统多目标跟踪算法在航迹初始阶段易受杂波干扰,提出一种交互多模型核预估数据流聚类的多目标跟踪算法(CE_DMTT)。对数据流进行在线聚类,并运用交互式多模型预估类核位置,缩小聚类搜索范围,同时引入Renyi熵,对聚类进行自适应提取,获取潜在航迹。然后基于潜在航迹运用多假设跟踪算法实现实时跟踪。仿真结果表明,该算法有效减少计算复杂度,提高系统实时性。
关键词:    多目标    交互多模型    Renyi熵    数据流聚类    多假设跟踪   
An Algorithm of Multi-Target Tracking Based on Clustering Data Stream
Ma Tianli, Wang Xinmin, Cao Yuyan, Huang Yu, Mu Lingxia
Department of Automatic Control, Northwestern Polytechnical University, Xi'an 710072, China
Abstract:
In order to deal with the clutter problem in the initial stage of traditional multi-target tracking,a data stream clustering mulitiple target algorithm based on Interactive Multi-Model core estimation(CE_DMTT) is proposed.Through estimating core position based on the Interactive Multi-Model to reduce the searching range of the cluster,the datastream is online clustering。In order to obtain potential track, the Renyi Entropy is introduced to adaptivly extract the cluster.Based on the obtained potential track, we use Multiple Hypotheses Tracking algorithm to implement real-time tracking.Simulation results and their analysis show preliminarily that the proposed algorithm can effectively reduce the computational complexity and improve System Real-time Performance.
Key words:    clustering algorithms    computational complexity    computer simulation    matrix algebra    covariance matrix    Gaussian noise(eletronic)    efficiency    entropy    estimation    mean square error    Monte Carlo methods    radar clutter    real time control    target tracking    vectors    data stream clustering    Interactive Multi-Model    Multiple Hypothesis Tracking,multi-target    Renyi entropy   
收稿日期: 2014-11-04     修回日期:
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作者简介: 马天力(1988—),西北工业大学博士研究生,主要从事导航、制导与控制研究。
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参考文献:
[1] Amini A, Wah T Y, Saboohi H. On Density-Based Data Streams Clustering Algorithms: A Survey[J]. Journal of Computer Science and Technology, 2014, 29(1): 116-141
[2] Khan K, Rehman S U, Aziz K, et al. DBSCAN: Past, Present and Future[C]//5th International Conference on Applications of Rigital Information and Web Technologies, 2014: 232-238
[3] Kirubarajan T, Barshalom Y. Probabilistic Data Association Techniques for Target Tracking in Clutter[J]. Proceedings of the IEEE, 2004, 92(3): 536-557
[4] Musicki D, Evans R. Joint integrated Probabilistic Data Association: JIPDA[J]. IEEE Trans on Aerospace and Electronic Systems, 2004, 40(3): 1093-1099
[5] Blackrnan S, House A. Design and Analysis of Modern Tracking Systems[M]. Boston, MA: Artech House, 1999
[6] He J Y, Yan Y J, Meng Q. Multiple Arrays Multiple Targets Data Fusion and Tracking Based on TDOA Measurement Using FCM-JPDA Algorithm[C]//2002 6th International Conference on Signal Processing, 2002: 1612-1616
[7] Ntoutsi I, Zimek A, Palpanas T, et al. Density-Based Projected Clustering over High Dimensional Data Streams[C]//12th SIAM International Conference on Data Mining, 2012: 987-998
[8] Cao F, Ester M, Qian W, et al. Density-Based Clustering over an Evolving Data Stream with Noise[C]//6th SIAM International Conference on Data Mining, 2006: 326-337
[9] Ruiz C, Menasalvas E, and Spiliopoulou M. C-Denstream: Using Domain Knowledge on a Data Stream[C]//Discovery Science. Springer Berlin Heidelberg, 2009: 287-301
[10] Deza M M, Deza E. Encyclopedia of Distances[M]. Springer, Berlin Heidelberg, 2009
[11] Lenzi E K, Mendes R S, da Silva L R. Statistical Mechanics Based on Renyi Entropy[J]. Physica A: Statistical Mechanics and Its Applications, 2000, 280(3): 337-345
[12] Crutchfield J P, Young K. Inferring Statistical Complexity[J]. Physical Review Letters, 1989, 63(2): 105
[13] Fu X, Jia Y, Du J, et al. New Interacting Multiple Model Algorithms for the Tracking of the Manoeuvring Target[J]. IET Control Theory & Applications, 2010, 4(10): 2184-2194
[14] Foo P H, Ng G W. Combining the Interacting Multiple Model Method with Particle Filters for Manoeuvring Target Tracking[J]. IET Radar, Sonar & Navigation, 2011, 5(3): 234-255
[15] Johnston L A, Krishnamurthy V. An Improvement to the Interacting Multiple Model (IMM) Algorithm[J]. IEEE Trans on Signal Processing, 2001, 49(12): 2909-2923
[16] Blackman S S. Multiple Hypothesis Tracking for Multiple Targets Tracking[J]. Aerospace and Electronic Systems Magazine of IEEE, 2004, 19(1): 5-18
[17] Muthumanikandan P, Vasuhi S, Vaidehi V. Multiple Maneuvering Target Tracking Using MHT and Nonlinear Non-Gaussian Kalman Filter[C]//International Conference on Signal Prosessing Communications and Netuorkiy, 2008: 52-56
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