Multi-step-ahead information-based feedback control for active binaural localization

Abstract

Binaural sound localization is known to be improved by incorporating the movement of the sensor. “Active” schemes based on this paradigm can overcome conventional limitations such as front-back ambiguity and source range recovery. Starting from a Gaussian prior on the relative position of a source, this paper determines the motion of a binaural sensor which leads to the most effective path for localization. To this aim, a reward function is defined as the conditional expectation, over the yet unknown N next observations, of the entropy of the N-step-ahead posterior pdf of the relative source position. The optimal motion of the binaural sensor is obtained from a constrained optimization problem involving the automatic differentiation of the reward function. The method is validated in simulation, and is being implemented on a real-life robotic test bed.