Towards Improved Sonar Performance Using Environment-Informed Sparse Sub-Array Processing
Alexandre L'Her, Angélique Drémeau, Florent Le Courtois, Gaultier Real, Xavier Cristol, Yann Stéphan
Abstract
In underwater acoustics, environmental fluctuations can lead to a loss of coherence in sonar arrays. Among the various approaches that can be considered to mitigate their effects, sub-array processing is a popular and intuitive one. However, it naturally results in sub-optimal performance. In this paper, we propose to enhance the approach in two ways. A first improvement is made by linking the parameters of the approach to those of the environment through a canonical correlation analysis. This leads in particular to an environment-informed calibration of the sub-arrays. A second improvement, more classical, is considered by using a sparse regularization on each sub-array processing. We show on real data the relevance of these choices and discuss the perspectives they open.
BibTeX
@inproceedings{icassp2023_towardsimproveds,
title = {Towards Improved Sonar Performance Using Environment-Informed Sparse Sub-Array Processing},
author = {Alexandre L'Her and Angélique Drémeau and Florent Le Courtois and Gaultier Real and Xavier Cristol and Yann Stéphan},
booktitle = {ICASSP 2023},
year = {2023}
}