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.
['Yang Song', 'Kainam Thomas Wong', 'Fangjiong Chen']
“Quasi-Blind” Calibration of an Array of Acoustic Vector-Sensors That Are Subject to Gain Errors/Mis-Location/Mis-Orientation