A one-step-ahead information-based feedback control for binaural active localization

Fundamental limitations of binaural localization, such as front-back ambiguity or distance non-observability, can be overcome by combining the sensed audio signals with the sensor motor commands into “active” schemes. Such strategies can rely on stochastic filtering. In this context, this paper addresses the determination of an admissible motion of a binaural head leading, on average, to the one-step-ahead most informative localization. To this aim, a constrained optimization problem is set up, which consists in maximizing the entropy of the next predicted measurement probability density function over a cylindric admissible set. The proposed optimum policy is validated on real-life robotic experiments.