Cooperative Filtering and Parameter Identification for Advection-Diffusion Processes Using a Mobile Sensor Network
IEEE Transactions on Control Systems Technology
This article presents an online parameter identification scheme for advection-diffusion processes using data collected by a mobile sensor network. The advection-diffusion equation is incorporated into the information dynamics associated with the trajectories of the mobile sensors. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensors so that the temporal variations in the field values can be estimated. This leads to a co-design scheme for state estimation and parameter identification for advection-diffusion processes that is different from comparable schemes using sensors installed at fixed spatial locations. Using state estimates from the constrained cooperative Kalman filter, a recursive least-square (RLS) algorithm is designed to estimate unknown model parameters of the advection-diffusion processes. Theoretical justifications are provided for the convergence of the proposed cooperative Kalman filter by deriving a set of sufficient conditions regarding the formation shape and the motion of the mobile sensor network. Simulation and experimental results show satisfactory performance and demonstrate the robustness of the algorithm under realistic uncertainties and disturbances.
National Science Foundation
Distributed parameter systems, Kalman filters, multi-robot systems, system identification
Jie You, Ziqiao Zhang, Fumin Zhang, and Wencen Wu. "Cooperative Filtering and Parameter Identification for Advection-Diffusion Processes Using a Mobile Sensor Network" IEEE Transactions on Control Systems Technology (2023): 527-542. https://doi.org/10.1109/TCST.2022.3183585
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.