Optimal strategies for the control of autonomous vehicles in data assimilation
This work addresses control strategies for autonomous vehicles in data assimilation, but it appears incremental as it focuses on specific linear flow scenarios without broad application.
The paper tackles the problem of computing optimal control paths for autonomous vehicles to infer velocity fields, using a locally optimal control algorithm based on minimizing posterior variance trace, and presents results for linear flows near hyperbolic fixed points.
We propose a method to compute optimal control paths for autonomous vehicles deployed for the purpose of inferring a velocity field. In addition to being advected by the flow, the vehicles are able to effect a fixed relative speed with arbitrary control over direction. It is this direction that is used as the basis for the locally optimal control algorithm presented here, with objective formed from the variance trace of the expected posterior distribution. We present results for linear flows near hyperbolic fixed points.