Experimental analysis of a game-theoretic formulation of target tracking. (April 2020)

• Record Type: Journal Article
• Authors: Yang, Yanbo; Moran, Bill; Wang, Xuezhi; Brown, Timothy C.; Williams, Simon; Pan, Quan
• Is Part Of: Automatica. Volume 114(2020)
• Publication Date: 2020-04
• Subjects: Fair game -- Target tracking -- Mutual information -- Pareto optimality -- Markov jump process; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2019.108793
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: Optimal trajectories for two platforms with similar dynamics are calculated using a game theoretic formulation. Each platform makes noisy observations of the kinematic state of the other. The objective of each is to maximise observable information about the other while minimising the information the other is able to acquire about it. That is to say, each platform maximises the mutual information between the expected future measurement of the opposing platform and the current likelihood of the state whilst minimising the estimated mutual information between potential measurements of itself by the other and its actual state. The multi-objective optimisation problem for each platform is converted to a single optimisation using the Pareto parameter to weigh the relative importance of the two information measures. The relationship between the two Pareto parameters, and different initial track initialisations is investigated. Remarkably this complex coupled system of two platforms exhibits, for suitably chosen values of the Pareto parameters, interesting cyclical behaviours that are worthy of further exploration.