Performance stability of high-resolution matched-field processors to sound-speed mismatch in a shallow-water environment

Abstract

The effects of variations in water sound-speed parameters on the performance of four matched-field processing algorithms (Bartlett, maximum-likelihood, sector-focusing, and multiple constraint) have been investigated. The SNAP propagation model was used to generate the replica acoustic pressure field for a shallow water channel with a depth variable sound-speed profile typical of a midlatitude summer environment. It was also used to simulate a ‘‘detected’’ field due to an acoustic source. These were then correlated using the four algorithms for selected degrees of mismatch of the water sound-speed profile. The maximum likelihood and multiple constraint estimators achieved good peak resolution and high accuracy for small degrees of mismatch. The maximum likelihood estimator deteriorated quickly as the mismatch increased. The multiple constraint estimator performed significantly better, but both failed to give correct location estimates at the higher mismatch values. In contrast, the sector focusing estimator continued to give accurate location estimates over the whole range of sound-speed mismatch, with slightly less peak resolution at small mismatch values. It also performed significantly better overall than the Bartlett estimator, which was found to give accurate location estimates over the whole range of mismatch values used, but with poor peak resolution against a background containing many high sidelobes.

Shallow water source localization using a mobile short horizontal array

“…In high SNR cases, the MV processor ambiguity surface provides a very sharp peak for the true source location, which flattens the background level and suppresses sidelobes elsewhere. However, this exceptional resolution capability is accompanied by an increased sensitivity to even a slight environmental mismatch [7,11].…”

“…1(a) shows the detailed values for all environmental parameters. The environmental mismatch is unavoidable and is likely the most outstanding obstacle to the general experimental application of MFP techniques [10][11][12]18,19]. At times the output of MFP deteriorates greatly even with low levels of the mismatch, especially for high resolution MFP processors.…”

“…The passive localization of an unknown acoustic source in shallow water is an important and challenging problem which has received considerable attention. Various schemes have been developed that work not only in idealized simulations but also in realistic scenarios; these schemes include plane-wave beamforming, wavefront curvature ranging, target motion analysis (TMA), multipath ranging, and matched-field processing (MFP) [2][3][4][5][6][7][8][9][10][11][12][13][14].…”