Kullback-Leibler Divergence based Sensor Placement Design for Kalman Filtering of Linear Dynamical Systems

In this work, we consider Kullback-Leibler divergence (KLD) based sensor placement design problem where Kalman filter is used to estimate the states of linear dynamical systems. Use of KLD enables sensor placement design while directly incorporating the end-user specified estimation accuracy, and is applicable to both Gaussian and non-Gaussian estimates case. These features are absent in the currently used design approaches. In particular, in the current work, we propose the optimal sensor placement to be the one that minimizes the KLD based distance of the estimation error density function (at large time instant) from the user specified reference density function. Application on case studies and comparison with existing design approaches demonstrates the utility of the proposed approach.