Prewhitening for improved detection by matched-field processing in ice-ridging correlated noise

Correlated ambient noise at an array can severely impair the ability of matched-field processing and conventional processing to detect and localize an acoustic source. When components of the correlated noise field are stationary, however, their effects can be reduced through the technique of prewhitening the data covariance matrix. Presented here are the results of a series of simulations in which prewhitening was applied to synthetic data generated for a correlated noise field. This study is relevant to prewhitening in noise fields that arise from distant localized biological sources, distant shipping, or ice ridging at low frequencies where the noise source remains spatially stable for hours. The present simulations modeled noise from ice ridging in the Arctic. In these simulations, a slanted linear array spanning the water column of a range-independent environment was used to enable full 3-D localization. The ability of prewhitening to enhance signal detection for low signal-to-noise cases was demonstrated for realistic conditions of covariance estimation of both the noise and data matrices. Under conditions where the intensity ratio of the ice-ridging noise to spatially uncorrelated noise was 20 dB, the sensor signal level required for detection was found to be 16 dB lower if prewhitening was employed, even when the source was positioned directly below one of the ice ridges. These results were obtained for the Bartlett processor, realistic time–bandwidth products, and experimentally determined environmental conditions.