Achieving high data rate robust communication in shallow and harbour underwater acoustic (UA) environments can be a demanding 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.
|Title of host publication||OCEANS 2019 - Marseille, OCEANS Marseille 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - Jun 2019|
|Event||2019 OCEANS - Marseille, OCEANS Marseille 2019 - Marseille, France|
Duration: 17 Jun 2019 → 20 Jun 2019
|Name||OCEANS 2019 - Marseille, OCEANS Marseille 2019|
|Conference||2019 OCEANS - Marseille, OCEANS Marseille 2019|
|Period||17/06/19 → 20/06/19|
Bibliographical noteFunding Information:
This work is supported by the NATO Science for Peace and Security Programme.
© 2019 IEEE.
- Underwater acoustic (UA) communications
- compressed sensing (CS)
- dictionary learning (DL)
- impulse noise
- shipping noise
ASJC Scopus subject areas
- Automotive Engineering
- Management, Monitoring, Policy and Law
- Water Science and Technology