Cooperative Filtering and Parameter Estimation for Polynomial PDEs using a Mobile Sensor Network
Publication Date
1-1-2022
Document Type
Conference Proceeding
Publication Title
Proceedings of the American Control Conference
Volume
2022-June
DOI
10.23919/ACC53348.2022.9867676
First Page
982
Last Page
987
Abstract
In this paper, a constrained cooperative Kalman filter is developed to estimate field values and gradients along trajectories of mobile robots collecting measurements. We assume the underlying field is generated by a polynomial partial differential equation with unknown time-varying parameters. A long short-term memory (LSTM) based Kalman filter, is applied for the parameter estimation leveraging the updated state estimates from the constrained cooperative Kalman filter. Convergence for the constrained cooperative Kalman filter has been justified. Simulation results in a 2-dimensional field are provided to validate the proposed method.
Funding Number
CMMI-1917300
Funding Sponsor
National Science Foundation
Department
Computer Engineering
Recommended Citation
Ziqiao Zhang, Wencen Wu, and Fumin Zhang. "Cooperative Filtering and Parameter Estimation for Polynomial PDEs using a Mobile Sensor Network" Proceedings of the American Control Conference (2022): 982-987. https://doi.org/10.23919/ACC53348.2022.9867676
