  The paper addresses the problem of time offset synchronization in the
presence of temperature variations, which lead to a non-Gaussian environment.
In this context, regular Kalman filtering reveals to be suboptimal. A
functional optimization approach is developed in order to approximate optimal
estimation of the clock offset between master and slave. A numerical
approximation is provided to this aim, based on regular neural network
training. Other heuristics are provided as well, based on spline regression. An
extensive performance evaluation highlights the benefits of the proposed
techniques, which can be easily generalized to several clock synchronization
protocols and operating environments.
