Cancelling tow ship noise using an adaptive model-based approach

Ship noise is a major contributor to towed array measurement uncertainties that can lead to large estimation errors. Many approaches ignore this problem, since they rely on inherent narrowband processing to remove these effects. The overall signal-to-noise ratio (SNR) available is therefore decreased making the signal extraction problem more difficult. In this paper we discuss the development of an adaptive model-based processor (AMBP) for signal enhancement from a set of noisy hydrophone measurements contaminated with tow ship noise. These results provide a solution to the adaptive joint cancellation/signal enhancement problem. Here we concentrate on the underlying theoretical development demonstrating the relationship between the canceller and model-based signal enhancer.