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.

翻译：本文探讨了在温度变化导致非高加索环境的情况下时间抵消同步的问题。在这方面,正常的Kalman过滤显示,其效果不理想。开发了一种功能优化方法,以大致最佳估计主子和奴隶之间的时钟抵消。根据定期的神经网络培训,为这一目标提供了数字近似值。根据螺旋回归,还提供了其他螺旋体。广泛的绩效评估强调了拟议技术的好处,这些技术很容易被推广到几个时钟同步协议和操作环境。