Search for: All records

The problem of synthesizing an optimal sensor selection policy is pertinent to a wide variety of engineering applications, ranging from event detection to autonomous navigation. In this paper, we consider such a synthesis problem in the context of linear-Gaussian systems. Particularly, we for- mulate the optimal sensor selection problem in terms of a value iteration over the continuous space of covariance matrices. To obtain a computationally tractable solution, we subsequently formulate an approximate sensor selection problem, which is solvable through a point-based value iteration over a finite “mesh” of covariance matrices with a user-defined bounded trace. In addition, we provide theoretical guarantees bounding the suboptimality of the sensor selection policies synthesized through this approximate value iteration. Finally, we analyze the efficacy of our proposed method through a numerical example comparing our method to known results.