CUED Publications database

Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods

Li, Q and Ahmad, BI and Godsill, SJ (2021) Sequential Dynamic Leadership Inference Using Bayesian Monte Carlo Methods. IEEE Transactions on Aerospace and Electronic Systems. ISSN 0018-9251

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Hierarchy and leadership interactions commonly occur in animal groups, crowds of people and in vehicle motions. Such interactions are often affected by one or more individuals who possess key domain information (e.g. final destination, environmental constraints and best routes) or pertinent traits (e.g. better navigation, sensing and decision making capabilities) compared with the rest of the group. This paper presents a framework for the automatic identification of group structure and leadership from noisy sensory observations of tracked groups. Accordingly, a new leader-follower model is developed which assumes the dynamics of the group to be a multivariate Ornstein-Uhlenbeck process with the designated leader(s) drifting to the destination and followers reverting to the leaders’ state. Sequential Monte Carlo (SMC) approaches, and specifically the sequential Markov chain Monte Carlo (SMCMC) approach, are adopted to infer, probabilistically, the evolving leadership structure. A Rao-Blackwellisation scheme is employed such that the kinematic state of the objects in the group is inferred in closed form by Kalman filtering. Experiments show that the proposed techniques can successfully determine the leadership structures in challenging scenarios with a corresponding enhancement in tracking accuracy through direct consideration of the leadership interactions of the group.

Item Type: Article
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 18 Jan 2021 20:12
Last Modified: 01 Apr 2021 04:52
DOI: 10.1109/TAES.2021.3054693