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Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking |
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| Affiliation: | 1. Department of Geotechnical Engineering, Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Tongji University, Shanghai, China;1. Faculty of Marine Technology, Amirkabir University of Technology, Hafez Avenue, Tehran 15914, Iran;2. Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais, 1049-001 Lisboa, Portugal;1. Civil Engineering Department, The Higher Technological Institute, Ramadan 10th city, Egypt;2. Basic Sciences Department, Banha University, Shobra, Cairo, Egypt;3. Deputy Secretary General, Supreme Council of Universities, and Structural Engineering Department, Cairo University, Giza, Egypt;1. Department of Mechanical Engineering, University of Western Macedonia, Bakola & Sialvera, 50100 Kozani, Greece;2. Research & Development Department, Kleemann Hellas S.A., Kilkis Industrial Area 61100, P.O. Box 25, Greece |
| Abstract: | We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis–Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland. |
| Keywords: | Multiple target tracking Rao-Blackwellized Monte Carlo data association Particle filtering Sequential Monte Carlo Particle MCMC Parameter estimation |
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