Monte Carlo Data Association for Multiple Target Tracking
2001 (English)In: Proceedings of the 2001 IEE International Seminar on Target Tracking: Algorithms and Applications, 2001, 13/1-13/5 p.Conference paper (Refereed)
The data association problem occurs in multiple target tracking applications. Since nonlinear and non-Gaussian estimation problems are solved approximately in an optimal way using recursive Monte Carlo methods or particle filters, the association step is crucial for the overall performance. We introduce a Bayesian data association method based on the particle filter idea and joint probabilistic data association (JPDA) hypothesis calculations. A comparison with classical EKF based data association methods such as the nearest neighbor (NN) method and the JPDA method is made. The NN association method is also applied to the particle filter method. Multiple target tracking using particle filtering increases the computational burden, therefore a control structure for the number of samples needed is proposed. A radar target tracking application is used in a simulation study for evaluation.
Place, publisher, year, edition, pages
2001. 13/1-13/5 p.
Bayes method, Kalman filter, Nonlinear estimation, Radar tracking
Engineering and Technology Control Engineering
IdentifiersURN: urn:nbn:se:liu:diva-29618DOI: 10.1049/ic:20010239Local ID: 14997OAI: oai:DiVA.org:liu-29618DiVA: diva2:250435
IEE International Seminar on Target Tracking: Algorithms and Applications, Enschede, The Netherlands, October, 2001