On Active Sensing methods for localization scenarios with range-based measurements

The work demonstrates how the methods of Active Sensing (AS), based on the theory of optimal experimental design, can be applied for a location estimation scenario. The simulated problem consists of several mobile and fixed nodes where each mobile unit is equipped with a gyroscope and an incremental path encoder and is capable to make a selective range measurement to one of several fixed anchors as well as to other moving tags. All available measurements are combined within a fusion filter, while the range measurements are selected with one of the AS methods in order to minimize the position uncertainty under the constraints of a maximum available measurement rate. Different AS strategies are incorporated into a recursive Bayesian estimation framework in the form of Extended Kalman and Particle Filters. The performance of the fusion algorithms augmented with the active sensing techniques is discussed for several scenarios with different measurement rates and a number of fixed or moving tags.