Multifidelity sensor placement in Bayesian state estimation problems

TL;DR

This paper develops novel algorithms for optimal sensor placement in Bayesian state estimation with sensors of varying costs and fidelities, improving efficiency and performance over random designs.

Contribution

Introduces new greedy and iterative algorithms for cost-constrained multifidelity sensor placement, leveraging linear algebra techniques and iterative optimization.

Findings

Algorithms outperform random sensor placement in benchmarks

Efficient rank-one update implementation reduces computational cost

Applicable to real-world problems like sea surface temperature reconstruction

Abstract

We study optimal sensor placement for Bayesian state estimation problems in which sensors vary in cost and fidelity, resulting in a budget-constrained multifidelity optimal experimental design problem. Sensor placement optimality is quantified using the D-optimality criterion, and the problem is approached by leveraging connections with the column subset selection problem in numerical linear algebra. We implement a greedy approach for this problem, whose computational efficiency we improve using rank-one updates via the Sherman-Morrison formula. We additionally present an iterative algorithm that, for each feasible allocation of sensors, greedily optimizes over each sensor fidelity subject to previous sensor choices, repeating this process until a termination criterion is satisfied. To the best of our knowledge, these algorithms are novel in the context of cost constrained multifidelity sensor placement. We evaluate our methods on several benchmark state estimation problems, including reconstructions of sea surface temperature and flow around a cylinder, and empirically demonstrate improved performance over random designs.