Dynamic compressed sensing estimation of time varying underwater acoustic channel

It has been recognized that, while the multipath structure offer the potential for sparsity exploitation by the means of compressed sensing, the rapidly time varying arrivals pose significant difficulties to the UnderWater Acoustic (UWA) channel estimation. Except the highly time varying arrivals caused by dynamic surface, generally there exist relatively stationary or slowly changing arrivals caused by direct path or bottom. By modeling the time varying UWA channels as sparse set consisting with constant and time-varying supports, the estimation of time varying UWA channel is transformed into a problem of Dynamic Compressed Sensing (DCS) sparse recovery. Via the combination of Kalman Filter (KF) and Compressed Sensing (CS), the Primal Dual Pursuit (PD-Pursuit) for the Dantzig Selector (DS) is adopted to pursue the complex domain solution of UWA channel estimation. Numerical simulations demonstrate the superiority of the proposed algorithm. In the form of a Channel-Estimation-based Decision-Feedback Equalizer (CE-DFE), the experimental results with the field data obtained in a shallow water acoustic communication experiment indicate that, the proposed algorithm outperforms the classic Least Square QR-factorization (LSQR) or Orthogonal Matching Pursuit (OMP) algorithms. Therefore, it is shown that the DCS estimation can improve the estimation performance effectively in time varying UWA channel.