An approach for safer navigation under severe hurricane damage

Eshghi, Mohammad; Schmidtke, Hedda R.

doi:10.1007/s40860-018-0066-1

Cited by 6 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…

Natural phenomena, such as forest fires, hurricanes, and ocean eddies, are complex spatial-temporal processes that are influenced by physical parameters like wind speed, humidity, flow direction, flow speed, and temperature (Eshghi and Schmidtke, 2018;Wei et al, 2019;Chen and Dames, 2020;Park et al, 2018). By understanding these phenomena in real-time, precision countermeasures such as rerouting firefighters/AUVs to the growing edge of a forest fire, monitoring and evacuating local areas that are at risk of hurricane damage, or decreasing the search area of floating plane-crash survivors by measuring and estimating ocean eddies can be effectively deployed (Radmanesh et al, 2021;Elston et al, 2010).

…”

mentioning

confidence: 99%

Distributed cooperative Kalman filter constrained by advection–diffusion equation for mobile sensor networks

Zhang

Mayberry

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

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.

show abstract

“…

…”

mentioning

confidence: 99%