The present invention relates to the field of
time synchronization, and relates to a precise
clock synchronization parameter tracking method based on an augmented Kalman neural network. In the method, for a multi-hop network
scenario where an ambient temperature changes and a link
transmission delay is asymmetric, taking into account the
impact of an accumulated asymmetric link
transmission delay in a multi-hop
network on clock synchronization accuracy, a multi-hop
network clock observation equation containing an accumulated asymmetric link
delay is derived, and on the basis of a quadratic polynomial model of a temperature and a
clock frequency offset, a recursive state equation of the clock
frequency offset and a temperature change is established; the evolution processes of an accumulated asymmetric
delay and an unknown
temperature coefficient are modeled as a first-order linear difference equation, and the accumulated asymmetric
delay and the unknown
temperature coefficient are augmented to a clock parameter tracking
state space model; and an augmented Kalman neural network
algorithm is used to realize joint tracking of a
clock phase offset and a clock
frequency offset of a slave node. This method employs a
hybrid data-model-driven approach to perform joint tracking of the
clock phase offset and frequency offset, thereby improving accuracy and robustness of clock parameter tracking.