The core of eLoran ground-based timing navigation systems is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for a complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on a Back-Propagation neural network (BPNN) for a complex meteorological environment, which realizes the function of directly mapping propagation delay fluctuation through meteorological factors. First, the theoretical influence of meteorological factors on each component of propagation delay is analyzed based on calculation parameters. Then, through the correlation analysis of the measured data, the complex relationship between the seven main meteorological factors and the propagation delay, as well as their regional differences, are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with that of the existing linear model and simple neural network model.

Demodulation and decoding are pivotal for the eLoran system’s timing and information transmission capabilities. This paper proposes a novel demodulation algorithm leveraging a multiclass support vector machine (MSVM) for pulse position modulation (PPM) of eLoran signals. Firstly, the existing demodulation method based on envelope phase detection (EPD) technology is reviewed, highlighting its limitations. Secondly, a detailed exposition of the MSVM algorithm is presented, demonstrating its theoretical foundations and comparative advantages over the traditional method and several other methods proposed in this study. Subsequently, through comprehensive experiments, the algorithm parameters are optimized, and the parallel comparison of different demodulation methods is carried out in various complex environments. The test results show that the MSVM algorithm is significantly superior to traditional methods and other kinds of machine learning algorithms in demodulation accuracy and stability, particularly in high-noise and -interference scenarios. This innovative algorithm not only broadens the design approach for eLoran receivers but also fully meets the high-precision timing service requirements of the eLoran system.

The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions.

In the field of modern navigation and positioning, the ground-based eLoran system, serves as a vital backup to the global navigation satellite system (GNSS), which is crucial for numerous key applications. Signal demodulation, integral to eLoran’s precision timing and information transmission, significantly affects system performance. Aiming at the pulse position modulation (PPM) characteristics of eLoran signals, this paper introduces an innovative phase spectrum smoothing demodulation (PSSD) algorithm, crafted to improve demodulation performance under complex noisy and interference-laden conditions. Following a systematic review of existing demodulation techniques in eLoran, this paper details the theoretical foundation, key steps, and significant impact of parameter selection for the PSSD algorithm. Then, the unique advantages in dealing with noise, continuous wave, and skywave interference are analyzed and verified. Through extensive experimental validation under various SNR and interference conditions, the PSSD algorithm shows significant superiority in demodulation performance compared with the traditional envelope phase detection (EPD) algorithm. The effectiveness of the PSSD algorithm in interference mitigation and its stable performance across diverse conditions confirm its potential to meet the high-precision timing requirements of eLoran systems, contributing to the advancement of modern communication systems.

The core of eLoran ground-based timing navigation system is the accurate measurement of groundwave propagation delay. However, meteorological changes will disturb the conductive characteristic factors along the groundwave propagation path, especially for complex terrestrial propagation environment, and may even lead to microsecond-level propagation delay fluctuation, seriously affecting the timing accuracy of the system. Aiming at this problem, this paper proposes a propagation delay prediction model based on Back-Propagation neural network (BPNN) for complex meteorological environment, which realizes the function of directly mapping propaga-tion delay fluctuation through meteorological factors. Firstly, the theoretical influence of meteoro-logical factors on each component of propagation delay is analyzed based on calculation parame-ters. Then, through the correlation analysis of the measured data, the complex relationship be-tween the seven main meteorological factors and the propagation delay, as well as their regional differences are demonstrated. Finally, a BPNN prediction model considering regional changes of multiple meteorological factors is proposed, and the validity of the model is verified by long-term collected data. Experimental results show that the proposed model can effectively predict the propagation delay fluctuation in the next few days, and its overall performance is significantly improved compared with the existing linear model and simple neural network model. eLoran; meteorological factor; propagation delay prediction model; Back-Propagation neural network;

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.