Regularized matched-mode localization with environmental mismatch

Abstract

This paper considers a new approach to matched-mode processing (MMP) for source localization. The MMP consists of decomposing far-field acoustic data to obtain the modal excitations, then matching these with modeled replica excitations. A potential advantage of MMP over matched-field processing (MFP) is that subsets of the complete mode set can be considered. For example, if geoacoustic properties are poorly known, the matching can be applied only to low-order modes that interact minimally with the seabed. However, modal decomposition can be ill posed and unstable if the sensor array does not adequately sample the acoustic field. For such cases, standard decomposition methods yield minimum-norm solutions that are biased towards zero. Although these methods provide mathematical solutions (stable solutions that fit the data), they may not represent physically meaningful solutions. The new approach of regularized MMP (RMMP) carries out an independent decomposition prior to comparison with the replica excitations for each grid point, using the replica itself as the prior estimate in a regularized inversion. This provides a more meaningful decomposition near the actual source location. In this paper, RMMP, MMP, and MFP are compared for realistic test cases, including various sensor array configurations, as well as environmental mismatch in seabed properties.