A History-based Stopping Criterion in Recursive Bayesian State Estimation

Abstract

In dynamic state-space models, the state can be estimated through recursive computation of the posterior distribution of the state given all measurements. In scenarios where active sensing/querying is possible, a hard decision is made when the state posterior achieves a pre-set confidence threshold. This mandate to meet a hard threshold may sometimes unnecessarily require more queries. In application domains where sensing/querying cost is of concern, some potential accuracy may be sacrificed for greater gains in sensing cost. In this paper, we (a) propose a criterion based on a linear combination of state posterior and its changes, (b) show that for discrete-valued state estimation scenarios the proposed objective is more likely to sort correct and incorrect estimates appropriately compared to just looking at the posterior, and finally (c) demonstrate that the method can lead to significant human intent estimation speed increase without significant loss of accuracy in a brain-computer interface application.