Improved compressed sensing estimation of block sparse underwater acoustic channel

For sparse underwater acoustic channels, compressed sensing methods can be adopted to improve the estimation performance. The classic l₀ or l₁ norm, however, are limited in describing the block sparse distributed characteristics of the underwater acoustic channel. We introduce the block sparsity identification term, i.e. block sparse approximated l₀ norm (BAL0) to address this problem. By adopting complex projected gradient method and then projecting the gradient solution to a set of the underwater acoustic channel solution space, an iterative algorithm is derived to solve the complex-field BAL0 norm channel estimation. Both the numerical simulation and experimental results show that the proposed algorithm has significant performance improvement compared with classic sparse signal recovery algorithms. By the derivation of the algorithm, simulations and at-sea experiment, one can conclude that the estimation quality of underwater acoustic channel can be improved by exploiting its block sparsity in compressed sensing reconstructions.