Abstract
Infotaxis was compared with maximum a posteriori (MAP) search in active sensing for varying sonar beam widths on one-dimensional grids. The active sensing model incorporated missed detections and false alarms typically found in sonar. Monte Carlo simulations were run across detection probabilities for a fixed false alarm probability, and experiments validated the simulations. Infotaxis outperformed MAP for high and low detection probabilities for medium beam widths. Infotaxis also showed faster recovery for missed first-detections compared to MAP for medium beam widths. Furthermore, Infotaxis exhibited information-maximizing behavior, confirming the strategy's effectiveness relative to MAP.