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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...
In this paper, we develop a game-theoretic framework to address data association for multiple-target tracking problems. We model the interaction among trackers as a game, by considering them as players, and the set of measurements as strategies. We develop utility functions for the players, and use a regret-based learning algorithm to find the equilibrium of the game. We will then use Monte Carlo...
We propose a particle filtering technique to track multiple maneuvering targets in the presence of clutter. We treat data association and state estimation, which are the two important sub-problems in tracking, as separate problems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility...
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