Blind separation of sources applied to convolutive mixtures in shallow water

In underwater acoustics the signal received by sensors is a mixture of different elementary sources, filtered by the environment. In blind separation of sources, we can isolate each source from different mixtures of sources without any a priori information, except for assuming statistical independence of the different sources. Two French researchers, J. Herault and C. Jutten had earlier proposed a neuromimetic solution to the problem. In our work, we use this solution to separate convolutive mixtures of simulated complex underwater signals in shallow water environment. To allowed the multipath identification a whitening step have to be introduced We propose a local whitening procedure that does not impact the separated signal output and preserve the signal characteristics This promising technique can be improved using iron causal whitening filters more adapted to the target environment.