Recursive Bayesian estimation of NFOV target using diffraction and reflection signals

This paper presents an approach to the recursive Bayesian estimation of non-field-of-view (NFOV) sound source tracking based on reflection and diffraction signals with an incorporation of optical sensors. The approach takes multi-modal sensoy fusion of a mobile robot, which combines an optical 3D environment geometrical description with a microphone array acoustic signal to estimate the target location. The robot estimates target location either in the field-of-view (FOV) or in the NFOV by fusion of sensor observation likelihoods. For the NFOV case, the microphone array provides reflection and diffraction observations to generate a joint acoustic observation likelihood. With the data fusion between the 3D description and the acoustic observation, the target estimation is performed in an unknown environment. Finally, the sensor observation combined with the motion model of the target iteratively performs tracking within a recursive Bayesian estimation framework. The proposed approach was tested with a microphone array with an RGB-D sensor in a controlled anechoic chamber to demonstrate the NFOV tracking capabilities for a moving target.