The JPDAF in practical systems: computation and snake oil

In this paper we look at various options for calculating target-measurement association probabilities and updating the state estimates in the Joint Probabilistic Data Association Filter (JPDAF). In addition to the "standard" methods, we look at other methods that try to improve the estimation accuracy by coupling the states, discarding certain joint association events, or by applying random finite set theory to change how the states are updated. We compare the performance of trackers based on several of these concepts to each other and to the PMHT, the MHT, and the GNN tracker. We also single out approaches that are "snake oil", in that they are either not suited for practical use, or that their complexity is higher than that of calculating the probabilities exactly. Additionally we show how the JPDAF* can be implemented to have a lower worst-case complexity than the regular JPDAF when the number of targets and/or observations is large. We also review some oft overlooked references on gating that are useful for implementations in real systems.

[1]  Samuel J. Davey,et al.  PDAF with multiple clutter regions and target models , 2003 .

[2]  Ba-Ngu Vo,et al.  A Consistent Metric for Performance Evaluation of Multi-Object Filters , 2008, IEEE Transactions on Signal Processing.

[3]  Lars S. Nyland,et al.  A Design Methodology for Data-Parallel Applications , 2000, IEEE Trans. Software Eng..

[4]  N. K. Bose,et al.  An efficient algorithm for data association in multitarget tracking , 1995 .

[5]  Marko Petkovšek,et al.  A=B : 等式証明とコンピュータ , 1997 .

[6]  N. K. Bose,et al.  A comprehensive analysis of 'Neural solution to the multitarget tracking data association problem' by D. Sengupta and R.A. Iltis (1989) , 1993 .

[7]  Krishna R. Pattipati,et al.  Fast data association using multidimensional assignment with clustering , 2001 .

[8]  David Thomas,et al.  The Art in Computer Programming , 2001 .

[9]  Ronald A. Iltis,et al.  Computing association probabilities using parallel Boltzmann machines , 1993, IEEE Trans. Neural Networks.

[10]  Fred Daum,et al.  MIMO radar: Snake oil or good idea? , 2008, 2009 International Waveform Diversity and Design Conference.

[11]  J. A. Roecker.  A class of near optimal JPDA algorithms , 1994 .

[12]  Gavin C. Cawley,et al.  On a Fast, Compact Approximation of the Exponential Function , 2000, Neural Computation.

[13]  D P Casasent,et al.  Fast JPDA multitarget tracking algorithm. , 1989, Applied optics.

[14]  Ronald A. Iltis,et al.  Neural solution to the multitarget tracking data association problem , 1989 .

[15]  N. K. Bose,et al.  Multitarget tracking in clutter: fast algorithms for data association , 1993 .

[16]  C. T. Gan.  A note on combination generators , 1985, TOMS.

[17]  Yee Ming Chen,et al.  A new data association algorithm for multi-target tracking in a cluttered environment , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[18]  Peter Willett,et al.  A Critical Look at the PMHT , 2009, J. Adv. Inf. Fusion.

[19]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[20]  Henk A. P. Blom,et al.  Probabilistic data association avoiding track coalescence , 2000, IEEE Trans. Autom. Control..

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

[22]  Selim G. Akl.  A Comparison of Combination Generation Methods , 1981, TOMS.

[23]  J. B. Collins,et al.  Efficient gating in data association with multivariate Gaussian distributed states , 1992 .

[24]  Yaakov Bar-Shalom,et al.  Dimensionless score function for multiple hypothesis tracking , 2007 .

[25]  Jon Louis Bentley,et al.  Data Structures for Range Searching , 1979, CSUR.

[26]  Zhijian Hu,et al.  Analysis of probabilistic data association filters in a real multitarget radar tracking environment , 1995, Defense, Security, and Sensing.

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

[28]  Aubrey B. Poore.  Complexity reduction in MHT/MFA tracking , 2005, SPIE Optics + Photonics.

[29]  Peter Willett,et al.  Set JPDA algorithm for tracking unordered sets of targets , 2009, 2009 12th International Conference on Information Fusion.

[30]  W. Marsden.  I and J , 2012 .

[31]  Robert J. Fitzgerald,et al.  Development of Practical PDA Logic for Multitarget Tracking by Microprocessor , 1986, 1986 American Control Conference.

[32]  D.J. Salmond,et al.  Mixture Reduction Algorithms for Point and Extended Object Tracking in Clutter , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[33]  İlke Türkmen Kerim Güney.  Computation of Association Probabilities for Single Target Tracking with the Use of Adaptive Neuro-Fuzzy Inference System , 2005 .

[34]  Yaakov Bar-Shalom,et al.  Sensor bias estimation in the presence of data association uncertainty , 2009, Optical Engineering + Applications.