Adaptive Observation Strategy for Dispersion Process Estimation Using Cooperating Mobile Sensors

Abstract

Efficient online state estimation of dynamic dispersion processes plays an important role in a variety of safety-critical applications. The use of mobile sensor platforms is increasingly considered in this context, but implies the generation of situation-dependent vehicle trajectories providing high information gain in real-time. In this paper, a new adaptive observation strategy is presented combining state estimation based on partial differential equation models of the dispersion process with a model-predictive control approach for multiple cooperating mobile sensors. In a repeating sequential procedure, based on the Ensemble Transform Kalman Filter, the uncertainty of the current estimate is determined and used to find valuable measurement locations. Those serve as target points for the controller providing optimal trajectories subject to the vehicles' motion dynamics and cooperation constraints. First promising results regarding accuracy and efficiency were obtained.

Centralized Ensemble-Based Trajectory Planning of Cooperating Sensors for Estimating Atmospheric Dispersion Processes

“…For details on the modeling, the reader is referred to [13]. An optimal control problem of the following form is set up and is solved in a receding horizon fashion to compute optimal control inputs for each vehicle:…”

“…For comparison, first, the adaptive observation approach presented in [13] is applied, but slightly modified assuming that the sensors measure at every time step. Then the problem is tackled applying the new motion planning approach involving local attraction points and improved target updates.…”

“…In contrast, this work adapts the strategy presented in [13] to exploit the advantages of a sensor system able to measure at every time step. Now that information is gathered en route, more importance is attached to the course of the trajectories leading to the target locations.…”

“…In previous work [13], a centralized adaptive observation strategy was proposed that combines PDE-based process models and optimally cooperating mobile sensors for online state estimation. Instead of solving a sophisticated optimal control problem, state estimation and vehicle control are considered separate problems that are linked in a repeating sequential procedure: The process estimate, based on a PDE model, is improved by assimilation of new sensor data using an ETKF approach.…”