We consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics (UWA), for instance, in applications requiring identi- fication of the acoustic channel, such as UWA communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identi- fying time-varying systems....

Performances of underwater acoustic communication (UAC) systems are strongly related to specific propagation conditions of the underwater channel. Due to their large variability, there is a need for adaptive matching of the UAC systems signaling to the transmission properties of the channel. This requires a knowledge of instantaneous channel characteristics, in terms of the specific parameters of stochastic models. The wide-sense...

We consider the problem of identification of communication channels with a mix of static and time-varying parameters. Such scenarios are typical, among others, in underwater acoustics. In this paper, we further develop adaptive algorithms built on the local basis function (LBF) principle resulting in excellent performance when identifying time-varying systems. The main drawback of an LBF algorithm is its high complexity. The subsequently...