Game theoretic data association for multi-target tracking with varying number of targets
conference contributionposted on 27.03.2019, 09:58 authored by Abdullah Daniyan, Yu GongYu Gong, Sangarapillai LambotharanSangarapillai Lambotharan
We investigate a game theoretic data association technique for multi-target tracking (MTT) with varying number of targets. The problem of target state-estimate-to-track data association has been considered. We use the SMC-PHD filter to handle the MTT aspect and obtain target state estimates. We model the interaction between target tracks as a game by considering them as players and the set of target state estimates as strategies. Utility functions for the players are defined and a regret-based learning algorithm with a forgetting factor is used to find the equilibrium of the game. Simulation results are presented to demonstrate the performance of the proposed technique.
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/K014307/1 and the MOD University Defence Research Collaboration (UDRC) in Signal Processing.
- Mechanical, Electrical and Manufacturing Engineering