Dynamic Underwater Acoustic Channel Tracking for Correlated Rapidly Time-varying Channels

TL;DR

This paper introduces a dynamic, time-variant state-space model combined with a forward-backward Kalman filter to enhance underwater acoustic channel tracking, especially in rapidly changing and rough sea conditions.

Contribution

It proposes a novel dynamic state-space model that tolerates model-mismatch and incorporates a forward-backward Kalman filter for improved tracking performance.

Findings

Significant improvement over conventional algorithms in rough sea conditions

Enhanced tracking accuracy for fast-varying channels

Model tolerance to slight correlation after decorrelation

Abstract

In this work, we focus on the model-mismatch problem for model-based subspace channel tracking in the correlated underwater acoustic channel. A model based on the underwater acoustic channel's correlation can be used as the state-space model in the Kalman filter to improve the underwater acoustic channel tracking compared that without a model. Even though the data support the assumption that the model is slow-varying and uncorrelated to some degree, to improve the tracking performance further, we can not ignore the model-mismatch problem because most channel models encounter this problem in the underwater acoustic channel. Therefore, in this work, we provide a dynamic time-variant state-space model for underwater acoustic channel tracking. This model is tolerant to the slight correlation after decorrelation. Moreover, a forward-backward Kalman filter is combined to further improve the tracking performance. The performance of our proposed algorithm is demonstrated with the same at-sea data as that used for conventional channel tracking. Compared with the conventional algorithms, the proposed algorithm shows significant improvement, especially in rough sea conditions in which the channels are fast-varying.