Noise cancellation for an autonomous underwater vehicle‐towed thin line array through recursive adaptive filtering

The digital thin line array (DTLA) developed at ARL, National University of Singapore have been integrated and tested with different autonomous underwater vehicles (AUV) and tested in the field to detect, localize and track underwater targets. For tow speeds not exceeding 4knots, it has been found that the flow noise is not a major contributor to performance reduction. However, noise generated by the propulsion system of some of the AUVs presents a major interferer to the detection capabilities of DTLA, especially, at low ambient noise conditions. The method proposed in the literature for platform noise cancellation employs a single adaptive filter. The number of the filer taps required in this case would be more than thousands due to the multi-path nature of the AUV noise in the environment of interest and the solution is difficult to converge. To mitigate the above problem, we propose a recursively adaptive strategy, which employs the least mean-square (LMS) algorithm. Our strategy is based on the fact ...