Adaptive mixture approximation for target tracking in clutter

Abstract

Target tracking is a state estimation problem common in many practical scenarios like air traffic control, autonomous vehicles, marine radar surveillance and so on. In a Bayesian perspective, when phenomena like clutter are present, most existing tracking algorithms must deal with association hypotheses which may grow in number over time. In that case, the posterior state distribution can quickly become computationally intractable, and approximations must necessarily be introduced. In this work, the impact of the number of hypotheses and of reduction procedures is investigated both in terms of computational resources and tracking performances. For this purpose, a recently developed adaptive mixture model reduction algorithm is considered in order to assess its performances when applied to the problem of single object tracking in the presence of clutter and to provide some interesting insights into the addressed problem.