Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

There has been a strong interest in emergency planning in response to an attack or accidental release of harmful chemical, biological, radiological or nuclear substances. Under such circumstances, it is of paramount importance to determine the location and release rate of the hazardous source to forecast the future harm it may cause and employ methods to minimize the disturbance. In this paper, a sensor data collection strategy is proposed whereby an autonomous mobile sensor is guided to address such a problem with a high degree of accuracy and in a short amount of time. First, the parameters of the release source are estimated using the Markov chain Monte Carlo sampling approach. The most informative manoeuvre from the set of possible choices is then selected using the concept of maximum entropy sampling. Numerical simulations demonstrate the superior performance of the proposed algorithm compared to traditional approaches in terms of estimation accuracy and the number of measurements required.