Shifeng Li

In the enhanced Loran (eLoran) system, the separation of skywave and groundwave is a crucial step to ensure the accuracy of signal processing, especially when facing challenges such as strong skywave interference and low signal-to-noise ratio (SNR). This paper proposes an upgraded algorithm that introduces the phase dimension based on traditional sky-wave separation technology, enhancing the comprehensiveness and applicability of the algorithm. Firstly, the algorithm utilizes an improved window function and spectral subtraction technique to estimate the characteristic parameters of the signal, and achieves the identification of skywave and groundwave through large number theory. Secondly, this paper proposes an adaptive second-order grid search matching algorithm that combines three dimensions: time delay difference, amplitude ratio, and phase difference, to accurately estimate the parameters of skywave and groundwave, effectively reconstruct and remove the skywave signal. This method not only improves the computational efficiency, but also enhances the comprehensive capture ability of the algorithm for signal characteristics by introducing phase information. Simulation results show that the algorithm can accurately identify and separate sky wave signals under strong sky wave interference. After introducing the phase dimension, the comprehensiveness of the algorithm is improved, and the accurate estimation of sky wave parameters is realized. This study provides a solid technical foundation for the stable realization of cycle identification and system functions in complex environments by improving the signal processing ability of eLoran system in harsh environments, and opens up a new technical path to improve the positioning accuracy and reliability of the system.