Statistical Shipping Noise Characterization and Mitigation for Underwater Acoustic Communications

By: Atanackovic L., Zhang R., Lampe L., DIamant R.
Published in: OCEANS 2019 – Marseille, OCEANS Marseille 2019
SDGs : SDG 14  |  Units: Marine Sciences  | Time: 2019 |  Link
Description: Achieving high data rate robust communication in shallow and harbour underwater acoustic (UA) environments can be a dema nding challenge in the presence of shipping noise. Noise generated from nearby passing ships can lead to impulsive agitations which impair UA communication systems. Utilizing the assumption that impulse noise exhibits sparsity, we realize a compressed sensing (CS) based framework for noise estimation exploiting the pilot sub-carriers of UA orthogonal frequency-division modulation systems. Under the CS framework, we propose the use of a empirical Bayesian approach which first characterizes the statistical properties of shipping noise prior to conceiving an estimate. In addition, we invoke the K-SVD algorithm for dictionary learning. K-SVD iteratively forms a sparse representation for the class of shipping noise signals, which is later used for noise estimation. Numerical results show that the empirical Bayesian based signal recovery algorithm yields the best performance for interference estimation. © 2019 IEEE.