通用航空集群的高精度时钟同步算法OA

High-precision clock synchronization is a fundamental technology enabling collaborative functions such as distributed sensing,formation control,and data fusion in general aviation swarms.However,in high-dynamic maneuvering scenarios,traditional round-trip time(RTT)synchronization methods suffer from significant accuracy degradation due to the coupling effects of relative motion-induced Doppler shifts and stochastic unequal reply time(URT)delays within airborne nodes.To address these challenges,this paper proposes a novel RTT clock synchronization algorithm that integrates relative-velocity compensation with a hybrid data-driven error correction mechanism.First,a kinematic model considering radial relative velocity is established to explicitly correct propagation delays caused by node mobility.Building on this,a batch-estimation-based delay modeling strategy is introduced.By extracting statistical features from multi-cycle timing data,this method calculates the equivalent processing delay sensitivity to eliminate systematic URT deviations.Furthermore,to address non-linear clock frequency drifts and complex environmental noise that traditional linear filters cannot resolve,a cascaded time-keeping architecture is developed.This architecture combines a Kalman filter(KF)for real-time state recursion with a Back-Propagation(BP)neural network for residual prediction.The BP network utilizes a lightweight topology to learn and compensate for non-linear errors based on inputs such as signal-to-noise ratio(SNR)and historical residuals.Extensive Monte Carlo simulations are conducted across continuous parameter spaces,including relative velocities up to 2 000 m/s and SNRs ranging from 4 dB to 20 dB.The numerical results demonstrate that the proposed algorithm achieves superior robustness and accuracy.Specifically,under strong URT interference(80 ns),the synchronization error remains stable below 0.25 ns.In low-SNR environments(4 dB),the root mean square error(RMSE)is controlled at approximately 0.2 ns,which represents a nearly tenfold improvement compared to the baseline.