Sliding-window homotopy adaptive filter for estimation of sparse UWA channels

In this paper, a sparse recursive least squares (RLS) adaptive filter is investigated in application to channel estimation in underwater acoustic (UWA) channels. The adaptive filter is based on sliding-window, homotopy, and dichotomous coordinate descent iterations. It is used in a multi-antenna receiver of an UWA communication system with guard-free orthogonal frequency division multiplexing (OFDM) signals and superimposed pilot symbols. More specifically, it is used for channel estimation in the channel-estimate-based equalizer. We compare the sliding-window homotopy RLS adaptive filter with the exponential-window homotopy and classic RLS algorithms. The results show that the proposed algorithm provides an improved performance compared to the other adaptive filters. The comparison is based on signals recorded on a 14-element vertical antenna array in sea trials at a distance of 105 km transmitted by a fast moving transducer. In these conditions, error-free transmission is achieved with a spectral efficiency of 0.33 bit/s/Hz.