Publication Date
1-1-2023
Document Type
Article
Publication Title
Frontiers in Robotics and AI
Volume
10
DOI
10.3389/frobt.2023.1175418
Abstract
In this paper, a distributed cooperative filtering strategy for state estimation has been developed for mobile sensor networks in a spatial–temporal varying field modeled by the advection–diffusion equation. Sensors are organized into distributed cells that resemble a mesh grid covering a spatial area, and estimation of the field value and gradient information at each cell center is obtained by running a constrained cooperative Kalman filter while incorporating the sensor measurements and information from neighboring cells. Within each cell, the finite volume method is applied to discretize and approximate the advection–diffusion equation. These approximations build the weakly coupled relationships between neighboring cells and define the constraints that the cooperative Kalman filters are subjected to. With the estimated information, a gradient-based formation control law has been developed that enables the sensor network to adjust formation size by utilizing the estimated gradient information. Convergence analysis has been conducted for both the distributed constrained cooperative Kalman filter and the formation control. Simulation results with a 9-cell 12-sensor network validate the proposed distributed filtering method and control law.
Funding Number
CMMI-1917300
Funding Sponsor
National Science Foundation
Keywords
cooperative control, distributed parameter systems, formation control, Kalman filter, mobile sensor networks
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Engineering
Recommended Citation
Ziqiao Zhang, Scott T. Mayberry, Wencen Wu, and Fumin Zhang. "Distributed cooperative Kalman filter constrained by advection–diffusion equation for mobile sensor networks" Frontiers in Robotics and AI (2023). https://doi.org/10.3389/frobt.2023.1175418
