A Constraint Generation Integer Programming Approach to Information Theoretic Sensor Resource Management

Many estimation problems involve sensors which can be actively controlled to alter the information received and utilized in the underlying inference task. In this paper, we discuss a novel integer programming method which exploits the submodularity of information theoretic estimation criterion to find an efficient solution to constructing an open loop plan for sensor resource management problems involving many independent objects. The integer programming formulation solves a sequence of simplified problems; the solution of each forms an upper bound to the full complexity problem. The updates performed between iterations may be viewed as steps in a constraint generation process, ensuring that the bound is successively tightened. An auxiliary problem also provides a lower bound to the optimal solution, and a solution attaining that bound, enabling early termination with a guaranteed near-optimal solution. Computational experiments demonstrate the benefit that the algorithm can provide in various planning problems.