'Quasi-Blind' calibration of an array of acoustic vector-sensors that are subject to gain errors/Mis-location/Mis-orientation

This paper advances a new 'quasi-blind' calibration algorithm to calibrate a multi-array network (MAN) of acoustic-vector-sensors, whose component-sensors may have non-ideal gain/phase responses, incorrect orientations, and imprecise locations. This proposed calibration is 'quasi-blind' in not requiring any prior knowledge/estimation of any training signal's arrival-angle. This proposed algorithm is computationally orders-of-magnitude more efficient than maximum-likelihood estimation. These advantages are achieved here by exploiting the acoustic vector-sensor's quintessential characters, to interplay between two complementary approaches of direction-finding: (1) customary interferometry between vector-sensors, and (2) 'acoustic particle-velocity-field normalization' DOA-estimation within each individual vector-sensor. Monte Carlo simulations verify the proposed algorithm's efficacy in 'quasi-blind' calibration and its aforementioned computational efficacy.