An enhanced long-range navigation (eLoran) system was selected as the backup of Global Navigation Satellite Systems (GNSS), and experts and scholars are committed to improving the accuracy of the eLoran system such that its accuracy is close to the GNSS system. A differential method called eLoran differential timing technology is applied to the eLoran system, which has been used in maritime applications of eLoran. In this study, an application of eLoran differential timing technology in a terrestrial medium is carried out. Based on the eLoran timing service error, the correlation of the timing service error is analyzed in theory quantitatively to obtain the range of the difference station in the ground. The results show that to satisfy the timing accuracy of 100 ns, the action range of eLoran difference station on the land needs to be less than 55 km. Therefore, the eLoran differential method is proposed, and in the difference station, the theoretical calculation is combined with the measurement of the signal delay to obtain the difference information, which is sent to the users to adjust the prediction delay and improve the eLoran timing precision. The experiment was carried out in the Guan Zhong Plain, and the timing error of the user decreased from 394.7287 ns (pre-difference) to 19.5890 ns (post-difference). The proposed method is found to effectively enhance the timing precision of the eLoran system within the scope of action.

The propagation of long wave signal is influenced by many factors, such as topography, geology, and weather. How to get the accuracy propagation delay has always been the bottleneck restricting the realization of high precision timing of long wave system. There are two methods to obtain the delay the calculation and measurement. There is Deviation between the calculation value and the real delay which is caused by the signal propagation path condition variation. For users in a range, the conditions are similar on the signal propagation path, there are the correlation in delay uncertainty among the users. Then the relevance of delay uncertainty is analyzed in theoretical, and a correction method of propagation delay is proposed. Similar to the DGPS, in the reference station, we measure time delay, calculate the offset, and send to users. The users around the reference station receive the value, and modify the calculation value to get the accuracy delay. The results show that it can effectively improve the accuracy of signal propagation delay. For the user, the closer is to the differential station, the higher the correction accuracy is.

The Long Range Navigation (Loran) system as a backup of the Global Navigation Satellite System (GNSS) is a good choice. The dominant deterioration factors of position accuracy are the pseudo-range measurement errors and the geometric dilution of precision (GDOP). This paper focuses on the algorithm integrated difference with pseudo-ranges to improve the position accuracy. Firstly, the theoretical prediction of propagation delay and raw measurement are compared. The results show that the measured pseudo-range consists of a constant term and a temporal term, which reflect the propagation situation along the true path. Secondly, a position solution algorithm based on a pseudo-range and difference is presented, exceeding the limit of a single chain. Finally, some simulation tests are implemented utilizing the new proposed position algorithm to verify the differential performance. This method can reduce the GDOP conveniently through increasing the number of transmitters. In view of the amplitude and characteristics of errors in measurement, systematic error and random noise are distinguished and discussed. The absolute accuracy responds to the pseudo-range bias that is different from geometric distance and repeatable accuracy is mainly influenced by random noise. The difference method can improve the absolute accuracy via the correction degree without changing the geometry of the transmitters.

Demodulation is a key technology for the enhanced Loran (eLoran) system to achieve positioning and timing, and it affects the final performance of the system. Based on the traditional matched correlation algorithm, this paper proposes a new matched correlation demodulation method with notch processing. Furthermore, by combining it with the pattern modulation of the eLoran system, the matched correlation integrate notch demodulation method is further modified to improve demodulation performance. Firstly, the data link of the eLoran system is introduced in detail, including the encoding and modulation processes, the influencing factors of received signals, and the evaluation methods in the demodulation process. Secondly, on the basis of the principle of the matched correlation (MC) demodulation algorithm, a matched correlation demodulation algorithm integrating notch processing (MC-NF) and a demodulation correlation algorithm combined with modulation patterns (PMC-NF) are proposed. And, an analysis of the key factors affecting demodulation performance is given. Next, the demodulation performance of the mentioned algorithms under the conditions of random noise, skywave, and in-band continuous wave interference is calculated in detail. A large number of experimental results show that notch processing performs excellently in suppressing random noise and in-band continuous wave interference, and it can greatly improve the demodulation performance of the traditional matched correlation algorithm. Moreover, PMC-NF is superior to MC-NF; approximately 2.8 dB at decoding the critical point.

The ASF grid is one of the important methods for improving the performance of the eLoran system. When an ASF grid is established, users can calculate ASF through the ASF grid database and bilinear interpolation algorithms, thereby obtaining high-precision ASF values. However, establishing an ASF grid is a complex process, involving not only extensive data collection tasks but also the cumbersome processing of the collected data. This paper investigates and analyzes algorithms for establishing ASF grids under different conditions. When the number of test points exceeds the number of grid vertices, grids built using interpolation algorithms—particularly the inverse bilinear interpolation algorithm—perform the best, while those constructed using the Kriging algorithm perform second best, and the inverse distance interpolation algorithm yields the largest errors. When the number of test points is less than the number of grid vertices, grids constructed using the Kriging algorithm perform the best, while the inverse distance interpolation algorithm produces the largest errors. As a result, the inverse bilinear interpolation algorithm is the best choice, when the test points are more than the grid points. The Kriging algorithm is recommended, when the test points are sparse.