Iterative joint integrated probabilistic data association for multitarget tracking

In situations with a significant number of targets in mutual proximity (close to each other), optimal multitarget data association approach suffers from the numerical explosion. This severely limits the applicability, i.e., the number of close targets that may be reliably tracked.We propose an iterative implementation of the joint integrated probabilistic data association (JIPDA) which allows a performance/computation resources tradeoff. This approach can also be incorporated into joint integrated track splitting (JITS). The iterations start with the single target integrated probabilistic data association (IPDA) and each subsequent iteration improves the approximation towards JIPDA, reaching the optimal multitarget solution within a finite number of iterations.

[1]  Peter Willett,et al.  The JPDAF in practical systems: approximations , 2010, Defense + Commercial Sensing.

[2]  Taek Lyul Song,et al.  Adaptive Clutter Measurement Density Estimation for Improved Target Tracking , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Taek Lyul Song,et al.  A multi scan clutter density estimator , 2013, Proceedings of the 16th International Conference on Information Fusion.

[4]  Anna Freud,et al.  Design And Analysis Of Modern Tracking Systems , 2016 .

[5]  Robin J. Evans,et al.  Fundamentals of Object Tracking , 2011 .

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

[7]  S. Stankovic,et al.  Integrated probabilistic data association (IPDA) , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[8]  Darko Musicki,et al.  Joint Integrated Probabilistic Data Association - JIPDA , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[9]  Y. Bar-Shalom,et al.  Tracking in a cluttered environment with probabilistic data association , 1975, Autom..

[10]  Robin J. Evans,et al.  Integrated probabilistic data association , 1994, IEEE Trans. Autom. Control..

[11]  B. Moran,et al.  Clutter map and target tracking , 2005, 2005 7th International Conference on Information Fusion.

[12]  R. Evans,et al.  Clutter map information for data association and track initialization , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[13]  Yaakov Bar-Shalom,et al.  Sonar tracking of multiple targets using joint probabilistic data association , 1983 .

[14]  Y. Bar-Shalom,et al.  The interacting multiple model algorithm for systems with Markovian switching coefficients , 1988 .

[15]  R.J. Evans,et al.  Multiscan Multitarget Tracking in Clutter with Integrated Track Splitting Filter , 2009, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Taek Lyul Song,et al.  Iterative Joint Integrated Probabilistic Data Association , 2013, Proceedings of the 16th International Conference on Information Fusion.

[17]  J. A. Roecker,et al.  Suboptimal joint probabilistic data association , 1993 .

[18]  R.J. Evans,et al.  Integrated track splitting filter - efficient multi-scan single target tracking in clutter , 2007, IEEE Transactions on Aerospace and Electronic Systems.

[19]  R.J. Evans,et al.  Multi-target tracking in clutter without measurement assignment , 2008, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[20]  D. Musicki,et al.  Tracking in clutter using IMM-IPDA-based algorithms , 2008, IEEE Transactions on Aerospace and Electronic Systems.