A Weiss-Weinstein lower bound based sensing strategy for active state tracking

Abstract

The problem of sensing strategy design for active state tracking is considered. The system state is modeled by a discrete-time, finite-state Markov chain, which is observed through Gaussian measurement vectors that are dynamically selected by a controller. To overcome the computational complexity associated with the optimal sensing strategy derived in our prior work, a sensing strategy based on the sequential Weiss-Weinstein lower bound (WWLB) is proposed. To this end, closedform WWLB formulae for our system model are obtained, while accommodating for multi-valued discrete parameters and control inputs. Numerical results validating the success of the proposed strategy on real data from a physical activity tracking application are provided.1 This problem is also referred to as active hypothesis testing or controlled sensing.