Dynamic plume tracking using mobile sensors

Abstract

Plume localization and prediction using mobile sensors is the primary contribution of this paper. Plume concentration values, measured by chemical sensors at different locations, are used to estimate the source of the plume. This is achieved by employing a stochastic approximation technique to localize the source and compare its performance with the nonlinear least squares method. The source location is then used as the initial estimate for the boundary tracking problem. Sensor measurements are used to estimate the parameters and states of the state space model of the dynamics of the plume boundary. The predicted locations are the reference inputs for the LQR controller. Measurements at the new locations (after the correction of the prediction error) are added to the set of data to refine the next prediction step. Interpolation, using the sensors locations, is used to approximate the boundary shape. An illustrative two-dimensional example is provided.