Optimal guidance and estimation of a 2D diffusion-advection process by a team of mobile sensors

TL;DR

This paper develops an optimization framework for guiding a team of mobile sensors to efficiently estimate a 2D diffusion-advection process, ensuring near-optimal solutions with proven convergence and applicability to heterogeneous sensor teams.

Contribution

It introduces a novel optimization approach combining Kalman-Bucy filtering and Pontryagin's principle for sensor guidance in diffusion-advection processes, with proven solution existence and convergence.

Findings

Optimal sensor guidance minimizes estimation covariance and mobility costs.

The framework guarantees convergence to the true optimal solution as approximation improves.

Simulations demonstrate effectiveness for single and multiple heterogeneous sensors.

Abstract

This paper describes an optimization framework to design guidance for a possibly heterogeneous team of multiple mobile sensors to estimate a spatiotemporal process modeled by a 2D diffusion-advection process. Owing to the abstract linear system representation of the process, we apply the Kalman-Bucy filter for estimation, where the sensors provide linear outputs. We propose an optimization problem that minimizes the sum of the trace of the covariance operator of the Kalman-Bucy filter and a generic mobility cost of the mobile sensors, subject to the sensors' motion modeled by linear dynamics. We establish the existence of a solution to this problem. Moreover, we prove convergence to the exact optimal solution of the approximate optimal solution. That is, when evaluating these two solutions using the original cost function, the difference becomes arbitrarily small as the approximation gets finer. To compute the approximate solution, we use Pontryagin's minimum principle after approximating the infinite-dimensional terms originating from the diffusion-advection process. The approximate solution is applied in simulation to analyze how a single mobile sensor's performance depends on two important parameters: sensor noise variance and mobility penalty. We also illustrate the application of the framework to multiple sensors, in particular the performance of a heterogeneous team of sensors.