Sliding window adaptive filter with diagonal loading for estimation of sparse UWA channels

In this paper, we propose a recursive least square (RLS) adaptive filter for sparse identification of underwater acoustic (UWA) channels. The adaptive filter is based on sliding window, diagonal loading, and dichotomous coordinate descent iterations. The adaptive algorithm possesses a complexity that is only linear in the filter length. The adaptive filter is used for channel estimation in an UWA communication system with guard-free orthogonal frequency division multiplexing (OFDM) signals and superimposed pilot symbols. We investigate and compare performance of various RLS adaptive filters and show that the proposed sliding window sparse RLS adaptive filter with diagonal loading demonstrates the best performance. We also show that adaptive filters with the sliding window outperform adaptive filters with the exponential window. The comparison has been done using signals recorded in a sea trial at a distance of 80 km transmitted by a fast moving transducer, resulting in fast-varying channel. In these conditions, a low-error-rate transmission is achieved at a data rate of 0.5 bit/s/Hz.