Building the method for multi-target tracking based on the combination of PHD filter and JPDA filter using particle filter in 3D mixed coordinate system85 views
Keywords:Multi-target tracking; Mixed coordinates; PHD combined JPDA; Particle filter; Non-Gaussian; Constant velocity model.
Changing the target number, non-linear measurement models and non-Gaussian noise faces a challenge to multi-target tracking problems which are factors affecting the accuracy, execution time and deciding the success of the method as well. In this paper, the authors present a method to solve these problems. Wherein, the motion of targets is represented in the mixed coordinate system 3D base on combining PHD (Probability Hypothesis Density) and JPDA (Joint Probability Data Association). This method can track multiple targets in the most general case, that is to change the target number, system model and measurement model which is non-linear as the noise is non-Gaussian. The result of this work can be applied to the real-time response system when the targets are moving in close distances with rapid maneuvering.
. X. R. Li and V. P. Jilkov, “A Survey of Maneuvering Target Tracking: Dynamic Models”, InProc. 2000 SPIE Conf. on Signal and Data Processing of Small Targets, vol. 4048, Orlando, Florida, USA, pp. 212-235, (2000).
. X. R. Li and V. P. Jilkov, “A Survey of Maneuvering Target Tracking—Part III: Measurement Models”, In Proc. 2001 SPIE Conf. on Signal and Data Processing of Small Targets, vol. 4473, San Diego, CA, USA, (2001).
. Anton Haug and Lauren Williams, “A Spherical Constant Velocity Model for Target Tracking in Three Dimensions”, IEEEAC Paper #1661, Version 1, (2011). DOI: https://doi.org/10.1109/AERO.2012.6187209
. Thopas E. Fortmann, Yaakov Bar-Shalom and Molly Scheffe, “Multi-target tracking using joint probabilistic data association”, 0191-2216/80/0000-0807$00.75 0 1980 IEE.
. Aliakbar Gorji Daronkolaei, Vahid Nazari, Mohammad Bagher Menhaj, and Saeed Shiry,”A Joint Probability Data Association Filter Algorithm for Multiple Robot Tracking Problems”, Amirkabir University of Technology, Tehran, Iran (2000)
. Y. Bar-Shalom, T. Kirubarajan and X. Lin, “Probabilistic Data Association Techniques for Target Tracking with Applications to Sonar, Radar and EO Sensors”, Electrical and Computer Engineering Department, University of Connecticut, Storrs CT 06269-2157; Electrical and Computer Engineering Department, McMaster University Hamilton, Ontario, Canada L8S 4K1 (2003)
. M. Jaward, L. Mihaylova, N. Canagarajah and D. Bull, “Multiple Object Tracking Using Particle Filters”, IEEEAC paper # 1280
. Liming Chen, Zhe Chen, Fuliang Yin, “A Novel Merging Algorithm in Gaussian Mixture Probability Hyp othesis Density Filter for Close Proximity Targets Tracking”, Journal of Information & Computational Science 8: 12, pp. 2283–2299, (2011).
. D. Smith, S. Singh, “Approaches to multisensor data fusion in target tracking: A survey”, IEEE Transactions on Knowledge and Data Engineering, 18(12), pp. 1696-1710, (2006). DOI: https://doi.org/10.1109/TKDE.2006.183
. C. Rasmussen, G. D. Hager, “Probabilistic data association methods for tracking complex visual objects”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), pp. 560-576, (2001). DOI: https://doi.org/10.1109/34.927458
. S. S. Blackman, “Multiple hypothesis tracking for multiple target tracking”, IEEE Aerospace and Electronic Systems Magazine, 19(1), pp. 5-18, (2004). DOI: https://doi.org/10.1109/MAES.2004.1263228
. J. Goutsias, R. Mahler, H. T. Nguyen, “Random Sets, Theory and Application”, Springer-Verlag, (1997). DOI: https://doi.org/10.1007/978-1-4612-1942-2
. R. Mahler, “Multitarget Bayes ﬁltering via ﬁrst-order multitarget moments”, IEEE Transactions on Aerospace and Electronic Systems, 39(4), pp. 1152-1178, (2003). DOI: https://doi.org/10.1109/TAES.2003.1261119
. Go o dman, R. Mahler, H. Nguyen, “Mathematics of Data Fusion”, Kluwer Academic Publishers, (1997). DOI: https://doi.org/10.1007/978-94-015-8929-1
. Jaipal R. Katkuri Vesselin P. Jilkov X. Rong Li, “A Comparative Study of Nonlinear Filters for Target Tracking in Mixed Coordinates”, Department of Electrical Engineering University of New Orleans New Orleans, LA 70148, USA, (2010). DOI: https://doi.org/10.1109/SSST.2010.5442834
. Nguyễn Thị Hằng, “Một thuật toán tối ưu bám quỹ đạo mục tiêu của bài toán quan sát đa mục tiêu trong trường hợp có mục tiêu bị che khuất”, Đại học Mỏ địa chất.
. Nguyễn Thị Hằng, “Thuật toán viterbi cải tiến và bài toán xác định số mục tiêu trong mô hình quan sát đa mục tiêu”, Tạp chí nghiên cứu khoa học và công nghệ quân sự.