Source Localization With Multiple Hydrophone Arrays via Matched-Field Processing

Abstract

This paper considers approaches to combining information from multiple arrays in matched-field processing (MFP) for underwater acoustic source localization. The standard approach is to apply conventional MFP for each array independently, and sum the resulting Bartlett ambiguity surfaces computed for each array; this approach assumes that individual arrays comprise calibrated sensors which are synchronized in time. However, if the relative calibration and/or time synchronization is known between some or all arrays, more informative multiple-array processors can be derived using maximum-likelihood methods. If the relative calibration between arrays is known, the observed variation in received signal amplitude between arrays provides additional information for matched-field localization which is absent in the standard processor. If synchronization is known between arrays, phase variations provide additional localization information. Multiple-array processors accounting for different levels of interarray information are derived and evaluated in terms of the probability of correct localization from Monte Carlo analyses for a range of signal-to-noise ratios and the number of frequencies for simulated shallow-water scenarios with multiple horizontal and/or vertical arrays. The analysis indicates that, dependent on array configurations, significant improvements in source localization performance can be achieved when including relative amplitude and/or phase information in the multiple-array processor. The improvement is reduced by environmental and array (calibration and synchronization) mismatch; however, this degradation can be partially mitigated by including additional frequencies in the processing.