A Comparison of Bayesian Filter Based Approaches for Patient Localization during Emergency Response to Crisis

In order to overcome the logistical impediments caused during mass casualty disasters, we had proposed a new emergency response system based on a location aware wireless sensor network in our previous work. In this paper, we have implemented two new Bayesian filter based algorithms called improved range-based Monte Carlo patient localization and range-based unscented Kalman filter patient localization for real time localization of large number of patients at the disaster site. The simulation in realistic conditions of both the algorithms is done using a random waypoint and a disaster management mobility model to identify their suitability for patient tracking. The new localization solution in tandem with the emergency response system shall facilitate efficient logistic support at the disaster site.