Probabilistic data association avoiding track coalescence
- 1 January 2000
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automatic Control
- Vol. 45 (2), 247-259
- https://doi.org/10.1109/9.839947
Abstract
For the problem of tracking multiple targets, the joint probabilistic data association (JPDA) approach has shown to be very effective in handling clutter and missed detections. The JPDA, however, tends to coalesce neighboring tracks and ignores the coupling between those tracks. Fitzgerald (1990) has shown that hypothesis pruning may be an effective way to prevent track coalescence. Unfortunately, this process leads to an undesired sensitivity to clutter and missed detections, and it does not support any coupling. To improve this situation, the paper follows a novel approach to combine the advantages of JPDA coupling, and hypothesis pruning into new algorithms. First, the problem of multiple target tracking is embedded into one filtering for a linear descriptor system with stochastic coefficients. Next, for this descriptor system, the exact Bayesian and new JPDA filters are derived. Finally, through Monte Carlo simulations, it is shown that these new PDA filters are able to handle coupling and are insensitive to track coalescence, clutter, and missed detections.Keywords
This publication has 21 references indexed in Scilit:
- Image fusion for tracking manoeuvring targetsInternational Journal of Systems Science, 1997
- Multisensor multitarget mixture reduction algorithms for trackingJournal of Guidance, Control, and Dynamics, 1994
- A class of near optimal JPDA algorithmsIEEE Transactions on Aerospace and Electronic Systems, 1994
- Multitarget tracking in clutter: fast algorithms for data associationIEEE Transactions on Aerospace and Electronic Systems, 1993
- Suboptimal joint probabilistic data associationIEEE Transactions on Aerospace and Electronic Systems, 1993
- Multiple target tracking using products of position measurementsIEEE Transactions on Aerospace and Electronic Systems, 1993
- Multiple target tracking based on symmetric measurement equationsIEEE Transactions on Automatic Control, 1992
- Tracking of crossing targets with imaging sensorsIEEE Transactions on Aerospace and Electronic Systems, 1991
- The interacting multiple model algorithm for systems with Markovian switching coefficientsIEEE Transactions on Automatic Control, 1988
- Sonar tracking of multiple targets using joint probabilistic data associationIEEE Journal of Oceanic Engineering, 1983