Distributed compressed sensing estimation of underwater acoustic multiple-input-multiple-output channels

MIMO (Multiple Input Multiple Output) technology provides a potential solution for high data rate under- water acoustic communications under limited bandwidth. However, simultaneous presence of multipath and co-channel interference (Co-channel interference, CoI) poses significant difficulty to estimation of acoustic MIMO channels with the conventional method such as classic algorithms or compressed sensing (CS) algorithms. To exploit the spatial correlation feature of the acoustic MIMO channels, the distributed compressed sensing (DCS) acoustic MIMO channel estimation model based on re-organizing of the MIMO measurement matrix is proposed. By discriminatively estimating the sparse components with the same time delay and those with different time delay, a novel DOMP (Discriminative Orthogonal Matching Pursuit) algorithm is designed to facilitate enhanced estimation of multipath components, as well as alleviation of the CoI. Numerical simulations as well as sea trial experiments are provided to demonstrate the superior performance of the proposed method.