Sparse and Structured Modelling of Underwater Acoustic Channel Impulse Responses

In this paper, we consider real-time modelling of an underwater acoustic channel impulse response (CIR), exploiting the inherent structure and sparsity of such channels. Building on the recent development to model acoustic channels using a Kronecker structure, we propose a sparse block updating conjugate gradient algorithm. The method initially compensate for the drift common in underwater measurements, to allow the CIR to be modelled as sparse, and then forms a first sparse structured update of the CIR. Should the signal-to-noise ratio of the measurement be high enough to allow for it, the estimate is then refined by relaxing first the assumed Kronecker structure, and then, if still improving the estimate, the sparsity assumption. The proposed method is evaluated using both simulated and measured underwater signals, clearly illustrating the preferable performance of the proposed method.