Adaptive identification of sparse underwater acoustic channels with a mix of static and time-varying parameters

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 identification 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 identifying time-varying systems. In this paper, we further improve the performance of the fLBF algorithm by exploiting properties of the system. Specifically, we propose an adaptive time-invariance test to identify whether a particular system tap is static or time-varying and exploit this knowledge for choosing the number of basis functions. We also propose a regularization scheme that exploits the system sparsity and an adaptive technique for estimating the regularization parameter. Finally, a debiasing technique is proposed to reduce an inherent bias of fLBF estimates. The high performance of the fLBF algorithm with the proposed techniques is demonstrated in scenarios of UWA communications, using numerical and real experiments. (C) 2022 The Authors. Published by Elsevier B.V.

Automated summary

This paper investigates the identification problem of sparse linear systems with a mix of static and time-varying parameters. Such systems are common in underwater acoustics, particularly in applications that require identifying the acoustic channel for underwater communication, navigation, and continuous-wave sonar. The paper improves the performance of the fast local basis function (fLBF) algorithm by exploiting properties of the system. Specifically, adaptive time-invariance testing, regularization techniques, and debiasing methods are proposed to enhance the algorithm's performance.