Observer control for bearings-only tracking using possibility functions

Bearings-only tracking using passive sensors is important for covert surveillance of moving targets. This paper adopts a mathematical formulation of bearings-only tracking in the framework of possibility theory, where uncertainties are represented using possibility functions, rather than usual probability distributions. Possibility functions have the capacity to deal robustly with partial (incomplete) specification of mathematical models and have been found particularly useful in model mismatch situations. The paper explores the design of reward functions which provide information gain in the context of observer motion control, in the framework of possibilistic recursive filter for bearings-only tracking. Numerical results demonstrate that in the presence of a model mismatch, the proposed framework performs better than the Bayesian probabilistic framework for stochastic filtering and control. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper focuses on bearings-only tracking using passive sensors, adopting possibility functions to represent uncertainties instead of probability distributions. It explores the design of reward functions based on possibility theory and shows that the proposed framework outperforms the Bayesian probabilistic framework in the presence of model mismatch.