Toward optimal placement of spatial sensors

TL;DR

This paper proposes an efficient sensor placement method for detecting Poisson-distributed targets, maximizing detection probability while addressing computational challenges through a submodular approximation.

Contribution

It introduces a novel submodular approximation of the detection probability objective, enabling effective greedy sensor placement under uncertainty.

Findings

The approximation is non-negative, submodular, and monotone.

Greedy algorithms perform well with the proposed approximation.

Numerical case study on seafloor sensors for ship detection.

Abstract

This paper addresses the challenges of optimally placing a finite number of sensors to detect Poisson-distributed targets in a bounded domain. We seek to rigorously account for uncertainty in the target arrival model throughout the problem. Sensor locations are selected to maximize the probability that no targets are missed. While this objective function is well-suited to applications where failure to detect targets is highly undesirable, it does not lead to a computationally efficient optimization problem. We propose an approximation of the objective function that is non-negative, submodular, and monotone and for which greedy selection of sensor locations works well. We also characterize the gap between the desired objective function and our approximation. For numerical illustrations, we consider the case of the detection of ship traffic using sensors mounted on the seafloor.