Sensor Selection

Optimal sensor selection is combinatorially complex and hence intractable for large scale problems. Under mild conditions, greedy heuristics have proven to achieve performance within a factor of the optimal. Mutual information, a commonly used reward in information theory, can lead to "myopic" selection since it makes no use of the costs assigned to measurements. In addition, the particular choice of the visit walk greatly affects the outcome. In this project, we will examine conditions under which cost-penalized mutual information may achieve similar guarantees to that of mutual information. Lastly, we will explore ways to make informed choices of the visit walk, examine whether locally optimizing exchange algorithms can improve the results of greedy approaches and work on finding more efficient ways to compute information rewards.