Performance comparisons of array invariant and matched field processing using broadband ship noise and a tilted vertical array.

Abstract

This paper compares the localization performance of array invariant (AI) and matched field processing (MFP) using a ship of opportunity radiating random noise (200-900 Hz) and a tilted vertical array. AI is a deterministic approach to source-range estimation (i.e., depth-blind), exploiting the dispersion characteristics of broadband signals with minimal/no knowledge of the environment in shallow water. It involves time-domain plane-wave beamforming to separate multiple coherent arrivals (eigenrays) in beam angle and travel time, called "beam-time migration," from which the source range is directly estimated. In contrast, MFP is a model-based approach that requires accurate knowledge of the environment and array geometry (e.g., array tilt) to generate "replicas" for all possible source locations, finding the best match in the two-dimensional ambiguity surface of range and depth. While AI and MFP are both sensitive to array tilt, AI is equipped with self-calibration capability to estimate the array tilt and source range simultaneously. With the array tilt information from AI incorporated, the performance of MFP for range estimation can be comparable to that of AI to such an extent that the environmental knowledge is accurate.