  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.
