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Title: Cooperative Filtering and Parameter Estimation for Polynomial PDEs using a Mobile Sensor Network
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.
Authors:
; ;
Award ID(s):
1934836 1828678 1849228
Publication Date:
NSF-PAR ID:
10359105
Journal Name:
Proceedings of the 2022 American Control Conference
Page Range or eLocation-ID:
982 to 987
Sponsoring Org:
National Science Foundation
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