基于交替极小化卡尔曼滤波的PTP时钟同步参数跟踪方法OA

Clock synchronization is a critical technology for ensuring the reliable operation of industrial networks.The precision time protocol(PTP)achieves high-accuracy time synchronization by exchanging messages containing precise timestamps.However,uncertainties arising during timestamp generation and transmission introduce noise,leading to timestamp deviations and a consequent degradation in PTP synchronization accuracy.To address this issue,this paper proposes an alternating minimization kalman filter(AMKF)-based joint clock parameter tracking method to mitigate synchronization errors caused by timestamp noise.The proposed AMKF approach employs linear difference equations to establish a reliable clock state-space model.During the prediction stage,an alternating minimization strategy is introduced to jointly account for timestamp noise and unknown observation noise,thereby enhancing the update process of the conventional Kalman filter.By adaptively adjusting the Kalman gain,the influence of timestamp deviations and unknown observation noise parameters can be effectively suppressed,enabling accurate and robust estimation of clock parameters.Simulation results show that the root mean square error(RMSE)of clock phase offset and clock frequency offset achieved by the AMKF method are 0.004615 ms and 0.000741,respectively.Compared with existing approaches,the proposed method reduces the RMSE by approximately two orders of magnitude,demonstrating strong robustness and superior performance in complex industrial networking environments.