Optimal Placement of Heterogeneous Sensors for Targets with Gaussian Priors

An optimal strategy for geometric sensor placement to enhance target tracking performance is developed. Recently, a considerable amount of work has been published on optimal conditions for single-update placement of homogeneous sensors (same type and same measurement quality) in which the targets are either assumed perfectly known or the target location uncertainty is averaged out via the expected value of the determinant of the Fisher information matrix (FIM). We derive conditions for optimal placement of heterogeneous sensors based on maximization of the information matrix to be updated by the heterogeneous sensors from an arbitrary Gaussian prior characterizing the uncertainty about the initial target location. The heterogeneous sensors can be of the same or different types (ranging sensors, bearing-only sensors, or both). The sensors can also make, over several time steps, multiple independent measurements of different qualities. Placement strategies are derived and their performance is illustrated via simulation examples.