Joint tracking and classification based on conditional joint decision and estimation

In joint tracking and classification (JTC) problems, both decision and estimation are involved and they affect each other. Good solutions for JTC require solving the two problems jointly. A joint decision and estimation (JDE) framework based on a generalized Bayes risk was recently proposed for solving the problem of inter-dependent decision and estimation. In the JDE framework, a conditional JDE (CJDE) risk was proposed, and the corresponding optimal solution was obtained. Due to the development of modern sensor technology, multisensor data with different characteristics are available. In this paper, we solve a JTC problem using multisensor data by the CJDE method. First, a dynamic JTC problem based on kinematic and attribute measurements is formulated as a JDE problem. To solve this problem, we propose a multiple-model recursive CJDE (RCJDE) method, which is an extension of the original RCJDE to the multisensor scenario. For joint performance evaluation, we suggest two joint performance metrics (JPM) for the cases with known and unknown ground truth, respectively. Simulation results demonstrate the effectiveness of the proposed RCJDE method. They show that the multisensor data based RCJDE can outperform the traditional two-step strategies in JPM.

[1]  LI X.RONG,et al.  Evaluation of estimation algorithms part I: incomprehensive measures of performance , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Hugh F. Durrant-Whyte,et al.  A decentralized Bayesian algorithm for identification of tracked targets , 1993, IEEE Trans. Syst. Man Cybern..

[3]  Kuo-Chu Chang,et al.  Target identification with Bayesian networks in a multiple hypothesis tracking system , 1997 .

[4]  T. Kurien.  Framework for integrated tracking and identification of multiple targets , 1991, IEEE/AIAA 10th Digital Avionics Systems Conference.

[5]  T. Kirubarajan,et al.  Joint detection and tracking of unresolved targets with monopulse radar , 2008, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Subhash Challa,et al.  Joint target tracking and classification using radar and ESM sensors , 2001 .

[7]  X. R. Li,et al.  Measures of performance for evaluation of estimators and filters , 2001 .

[8]  Branko Ristic,et al.  On target classification using kinematic data , 2004, Inf. Fusion.

[9]  Ming Yang,et al.  Joint tracking and classification based on bayes joint decision and estimation , 2007, 2007 10th International Conference on Information Fusion.

[10]  Hans Driessen,et al.  Integrated tracking and classification: an application of hybrid state estimation , 2001, SPIE Optics + Photonics.

[11]  Wei Mei,et al.  Simultaneous tracking and classification: a modularized scheme , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Zhansheng Duan,et al.  Comprehensive evaluation of decision performance , 2008, 2008 11th International Conference on Information Fusion.

[13]  X. Rong Li,et al.  Optimal bayes joint decision and estimation , 2007, 2007 10th International Conference on Information Fusion.

[14]  A. Farina,et al.  Joint tracking and identification algorithms for multisensor data , 2002 .

[15]  J.A. O'Sullivan,et al.  Automatic target recognition using kinematic priors , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[16]  X. Rong Li,et al.  Conditional Joint Decision and Estimation With Application to Joint Tracking and Classification , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  Huosheng Hu,et al.  Multisensor-Based Human Detection and Tracking for Mobile Service Robots , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[19]  Simon Maskell.  Joint Tracking of Manoeuvring Targets and Classification of Their Manoeuvrability , 2004, EURASIP J. Adv. Signal Process..

[20]  V. Jilkov,et al.  Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[21]  Xiaodong Wang,et al.  Joint multiple target tracking and classification in collaborative sensor networks , 2004, ISIT.

[22]  Lang Hong,et al.  Wavelets feature aided tracking (WFAT) using GMTI/HRR data , 2003, Signal Process..

[23]  Yu Liu,et al.  Recursive joint decision and estimation based on generalized Bayes risk , 2011, 14th International Conference on Information Fusion.

[24]  Donka Angelova,et al.  Sequential Monte Carlo algorithms for joint target tracking and classification using kinematic radar information , 2004 .

[25]  Hongbin Zha,et al.  Tracking Generic Human Motion via Fusion of Low- and High-Dimensional Approaches , 2013, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[26]  W. Marsden.  I and J , 2012 .

[27]  Y. Bar-Shalom,et al.  Tracking with classification-aided multiframe data association , 2003, IEEE Transactions on Aerospace and Electronic Systems.

[28]  Thia Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software , 2001 .

[29]  X. Rong Li,et al.  Joint tracking and classification based on recursive joint decision and estimation using multi-sensor data , 2014, 17th International Conference on Information Fusion (FUSION).

[30]  X. Rong Li,et al.  Extended object tracking and classification based on recursive joint decision and estimation , 2013, Proceedings of the 16th International Conference on Information Fusion.