A method for joint estimation of TOA and carrier phase for eLoran receivers

By performing station chain identification and epoch folding on the eLoran signal, combined with baseband phase measurement and multi-model adaptive Kalman filtering, the accuracy and stability problems of carrier phase tracking in complex environments of traditional eLoran receivers are solved, and highly robust carrier phase tracking and timing output are achieved.

CN121934119BActive Publication Date: 2026-07-21NAT TIME SERVICE CENT CHINESE ACAD OF SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NAT TIME SERVICE CENT CHINESE ACAD OF SCI
Filing Date
2026-03-30
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Traditional eLoran receivers struggle to balance carrier phase tracking accuracy and stability in low signal-to-noise ratio or dynamically changing environments. Kalman filtering algorithms may cause filter divergence when noise modeling is insufficient. Existing technologies are unable to achieve robust carrier phase tracking in complex interference environments.

Method used

By identifying the eLoran signal and locking onto the standard GRI, epoch folding and TOA two-level refinement estimation are performed. Combining baseband phase measurement and Kalman filtering framework, a state vector is constructed and a multi-model adaptive Kalman filter is adopted. An adaptive expansion strategy is introduced to suppress impulse interference, thereby achieving joint estimation of TOA and carrier phase.

Benefits of technology

It significantly improves the robustness and stability of the eLoran receiver in complex electromagnetic environments, enhances the continuity and accuracy of timing output, and enables robust carrier phase tracking under dynamic conditions.

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Abstract

The application belongs to the technical field of communication. The application provides a TOA and carrier phase joint estimation method for an eLoran receiver. The disclosure embodiment utilizes pulse group period repetition to complete station chain identification and GRI locking, improves effective signal-to-noise ratio through epoch folding, and adopts "energy matching coarse estimation-coherent correlation refinement-peak parabolic interpolation" to construct a sub-sampling level TOA observation; meanwhile, coherent down-conversion accumulation is used to form a baseband phase observation and phase ambiguity branch selection and unwinding are performed. A unified state space model containing TOA bias, drift rate, baseband phase and frequency bias is established, joint Kalman filter recursion of TOA and phase is realized, and multi-model adaptive Kalman filter and innovation likelihood adaptive weighting, measurement noise inflation are introduced to suppress outlier observations.
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