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Publisher: AIMS
Languages: English
Types: Article
The probability hypothesis density (PHD) methodology is widely used by the research community for the purposes of multiple object tracking. This problem consists in the recursive state estimation of several targets by using the information coming from an observation process. The purpose of this paper is to investigate the potential of the PHD filters for real-time traffic state estimation. This investigation is based on a Cell Transmission Model (CTM) coupled with the PHD filter. It brings a novel tool to the state estimation problem and allows to estimate the densities in traffic networks in the presence of measurement origin uncertainty, detection uncertainty and noises. In this work, we compare the PHD filter performance with a particle filter (PF), both taking into account the measurement origin uncertainty and show that they can provide high accuracy in a traffic setting and real-time computational costs. The PHD filtering framework opens new research avenues and has the abilities to solve challenging problems of vehicular networks.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] M. Arulampalam, S. Maskell, N. Gordon and T. Clapp, A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE Transactions on Signal Processing, 50, 174-188 (2002).
    • [2] G. Battistelli, L. Chisci, S. Morrocchi, F. Papi, A. Benavoli, A. Di Lallo, A. Farina and A. Graziano, Traffic intensity estimation via PHD filtering, In Proc. 5th European Radar Conf., Amsterdam, The Netherlands, 340-343, (2008).
    • [3] A. Ben Aissa, J. Sau, N-E. El Faouzi and O. De Mouzon, Sequential Monte Carlo Traffic Estimation for Intelligent Transportation System: Motorway Travel Time Prediction Application, In Proc. of the 2nd ISTS, Lausanne, Switzerland (2006).
    • [4] R. Billot, N-E. El Faouzi, J. Sau and F. De Vuyst, Integrating the impact of rain into traffic management: online traffic state estimation using sequential Monte Carlo techniques, In Transportation Research Records (2010).
    • [5] Z. Chen, Bayesian filtering: From Kalman filters to particle filters, and beyond, Adaptive Systems Lab., Technical Report, McMaster University, ON, Canada (2003).
    • [6] M. Canaud, N-E. El Faouzi and J. Sau, Reservoir-based urban traffic modeling for travel time estimation: sensitivity analysis and case study, In Proc. of the 91th Transportation Research Board Annual Meeting,Washington D.C., USA, (2012).
    • [7] C. Daganzo, The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory, Transportation Research B, 28, 269-287 (1994).
    • [8] A. Doucet, Monte Carlo methods for Bayesian estimation of hidden Markov models. Application to radiation signals, PhD thesis, Universit Paris-Sud, Orsay (1997).
    • [9] A. Doucet, On sequential simulation-based methods for Bayesian filtering, Departement of Engineering, Technical report CUED/F-INFENG/TR.310, Cambridge University (1998).
    • [10] A. Doucet, N. De Freitas and N. Gordon, Sequential Monte Carlo Methods in Practice, Springer (2001).
    • [11] N-E. El Faouzi, Research Needs for Real Time Monitoring, Surveillance and Control of Road Networks under Adverse Weather Conditions, Research Agenda for the European Cooperation in the field of scientific and technical research (COST), (2007) - www.COST-TU0702.org.
    • [12] O. Erdinc, P. Willett, and Y. Bar-Shalom, Probability hypothesis density filter for multitarget multisensor tracking, In Proc. of the 8th Int. Conf. on Information Fusion, 146-153, Philadelphia, PA, USA (2005).
    • [13] K. Gilholm, S. Godsill, S. Maskell and D. Salmond, Poisson models for extended target and group tracking, In Proc. SPIE: Signal and Data Processing of Small Targets 5913, 230-241, San. Diego, CA, USA (2005).
    • [14] K. Gilholm and D. Salmond, Spatial distribution model for tracking extended objects, In Proc. IEEE on Radar, Sonar and Navigation, 152, 364 - 371, (2005).
    • [15] A. Gning, L. Mihaylova and F. Abdallah, Mixture of uniform probability density functions for non linear state estimation using interval analysis, In Proc. of the 13th Int. Conf. on Information Fusion, Edinburgh, UK (2010).
    • [16] A. Gning, B. Ristic and L. Mihaylova, A box particle filter for stochastic set-theoretic measurements with association uncertainty, In Proc. of the 14th Int. Conf. on Information on Fusion, Chicago, IL, USA (2011).
    • [17] A. Gning, B. Ristic and L. Mihaylova, Bernouilli particle/box particle filters for detection and tracking in the presence of triple uncertainty, IEEE Trans. Signal Processing, 60, 2138 - 2151, (2012).
    • [18] A. Hegyi, D. Girimonte, R. Babuska and B. De Schutter, A comparison of filter configurations for freeway traffic state estimation, In Proc. of the 2006 IEEE Intelligent Transportation Systems Conference (ITSC 2006), 1029 - 1034, Toronto, Canada (2006).
    • [19] R. Juang and P. Burlina, Comparative performance evaluation of GM-PHD filter in clutter, In Proc. of the 12t Internatiinal Conf. on Information Fusion, 1195 - 1202, (2009).
    • [20] S. Julier and J. Uhlmann, A new extension of the Kalman filter to nonlinear systems, In International Symposium on Aerospace/Defense Sensing, Simulation and Controls, 182-193, Orlando, FL, USA (1997).
    • [21] R. Kalman, A new approach to linear filtering and prediction problems, Journal of Basic Engineering, 82, 35-45 (1960).
    • [22] J. Lebacque, The Godunov scheme and what it means for first order traffic flow models, In Proc. of the 13th nternaional symposium on transportation and traffic theory (ISTTT), 647-677, (1995).
    • [23] R. Mahler, Multitarget Bayes filtering via First-order Multitarget Moments, IEEE Transactions on Aerospace and Electronic Systems, 39, 1152-1178 (2003).
    • [24] R. Mahler, Statistical multisources multitarget information fusion, Artech House, 2007.
    • [25] R. Mahler, PHD filters for nonstandard targets, I: Extended targets, In Proc. of the 12th International Conference on Information Fusion, 914-921, Seattle, WA, USA (2009).
    • [26] R. Mahler, B-T. Vo and B-N. Vo, CPHD Filtering With Unknown Clutter Rate and Detection Profile, IEEE Transactions on Signal Processing, 59, 3497-3513 (2011).
    • [27] L. Mihaylova and R. Boel, A particle filter for freeway traffic estimation, In Proc. of the 43rd IEEE Conference on Decision and Control, 2, 2106-2111, Atlantis, Paradise Island, Bahamas (2004).
    • [28] L. Mihaylova, R. Boel and A. Hegyi, Freeway Traffic Estimation within Recursive Bayesian Framework, Automatica, 43(12), 290-300 (2007).
    • [29] L. Mihaylova, A. Hegyi, A. Gning and R. Boel, Parallelized Particle and Gaussian Sum Particle Filters for Large Scale Traffic Systems, IEEE Transactions on Intelligent Transportation Systems. Special Issue on Emergent Cooperative Technologies in Intelligent Transp. Systems, 13(1), 36 - 48, (2012).
    • [30] K. Panta, B. Vo, S. Singh and A. Doucet, Probability hypothesis density filter versus multiple hypothesis tracking, In Proceedings of SPIE, Vol. 5429, 284-295 (2004).
    • [31] B. Ristic, M. Arulampalam and N. Gordon,, Beyond the Kalman Filter: Particle Filters for Tracking Applications, Artech House, Boston (2004).
    • [32] B. Ristic, D. Clark and B. Vo,, Improved SMC implementation of the PHD filter, In Proc. of the 13th International Conference on Information Fusion, Edinburgh, UK (2010).
    • [33] J. Sau, N-E. El Faouzi and O. De Mouzon, Particle-filter traffic state estimation and sequential test for real-time traffic sensor diagnosis, In Proc. of ISTS'08 Symposium, Queensland (2008).
    • [34] M. Schikora, A. Gning, L. Mihaylova, D. Cremers and W. Koch, Box-Particle PHD Filter for Multi-Target Tracking, IEEE Trans. on Aerospace and Electronic Systems, to appear (2013).
    • [35] H. Sidenbladh, Multi-target particle filtering for the probability hypothesis density, In Proc. 6th Int'l Conf. on Information Fusion, Cairns, Australia (2003).
    • [36] A. Sumalee, R.X. Zhong, T.L. Pan, and W.Y. Szeto, Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment, Transportation Research Part B, 45, 507-533 (2011).
    • [37] X. Sun, L. Munoz and R. Horowitz, Highway traffic state estimation using improved mixture Kalman filters for effective ramp metering control, In Proc. of th 42nd IEEE Conf. on Decision and Control, 6333-6338, Maui, Hawaii, USA (2003).
    • [38] J. Sussman, Introduction to Transportation Problems, Artech House, Norwood, Masachussets, 2000.
    • [39] B. Vo, S. Singh and A. Doucet, Sequential Monte Carlo methods for multi-target filtering with random finite sets, IEEE Trans. Aerospace and Electronic Systems, 41, 1224-1245 (2005).
    • [40] B. Vo and W. Ma, The Gaussian mixture probability hypothesis density filter, IEEE Trans. Signal Processing, 54, 4091-4104 (2006).
    • [41] B-T. Vo, B-N. Vo and A. Cantoni, Analytic implementations of the cardinalized probability hypothesis density filter, IEEE Trans. Signal. Processing, 55, 3553-3567 (2007).
    • [42] N.-N. Vo, B.-T. Vo, and D. Clark, Bayesian Multiple Target Tracking Using Random Finite Sets, Ch. 3 in Integrated Tracking, Classification, and Sensor Management: Theory and Applications, Eds. M. Mallick, V. Krishnamurthy, and B.-N. Vo, 75-125. John Wiley & Sons, 2012.
    • [43] Y. Wang, M. Papageorgiou and A. Messmer, Real-time freeway traffic state estimation based on extended Kalman filter: A case study, Transportation Science, 41, 167-181 (2007).
    • [44] Y. Wang, M. Papageorgiou, A. Messmer, P. Coppola, A. Tzimitsi and A. Nuzzolo, An adaptive freeway traffic state estimator, Automatica, 45, 10-24 (2009).
    • [45] N. Whiteley, S. Singh and S. Godsill, Auxiliary particle implementation of the probability hypothesis density filter, IEEE Trans. on Aerospace and Electronic Systems, 46, 1437-1454 (2010).
    • [46] D. Work, S. Blandin, O-P. Tossavainen, B. Piccoli, and A. Bayen, A distributed highway velocity model for traffic state reconstruction, Applied Mathematics Research eXpress, 2010, 1-35 (2010).
    • [47] D. Work, O.-P. Tossavainen, S. Blandin, A. M. Bayen, T. Iwuchukwu and K. Tracton, An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices, Proceedings of CDC, 5062-5068, (2008).
    • [48] T. Zajic and R. Mahler, Particle-systems implementation of the PHD multitarget tracking filter, In Proceedings of SPIE, Signal Processing, Sensor Fusion, and Target Recognition, XII, Vol. 5096, 291-299, Bellingham (2003).
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