A Three-Layer Adaptive Kalman Filter Approach for Multi-Source Time Fusion Using Temperature-Compensated Oscillators with GNSS and eLoran Backup

This paper proposes a three-layer adaptive Kalman filter-based multi-source time fusion method for constructing a high-precision, continuous, and robust chip-level time reference. A digitally temperature-compensated TCXO is used as the short-term local time reference, and the Allan variance is introduced to characterize oscillator frequency stability and model the process-noise covariance. For medium-term correction, BeiDou observations are fused with oscillator prediction through an adaptive Kalman filter. A reliability score based on C/N0, DOP, pseudo-range residuals, and other quality indicators is used to dynamically adjust the observation-noise covariance and Kalman gain. When BeiDou signals become unreliable or unavailable, eLoran is introduced as a backup timing source to maintain continuous output. In addition, Kalman filter residuals are fed back to the TCXO temperature-compensation module, forming a closed-loop correction mechanism to suppress residual frequency drift and accumulated timing errors. Experimental results show that the proposed method significantly reduces timing errors and improves continuity, stability, and recovery capability under BeiDou degradation and outage conditions.