The eLoran system is an international standardized positioning, navigation, and timing service system, which can complement global navigation satellite systems to cope with navigation and timing warfare. The eLoran receiver measures time-of-arrival (TOA) through cycle identification, which is key in determining timing and positioning accuracy. However, noise and skywave interference can cause cycle identification errors, resulting in TOA-measurement errors that are integral multiples of 10 μs. Therefore, this article proposes a cycle identification method in the joint time–frequency domain. Based on the spectrum-division method to determine the cycle identification range, the time–domain peak-to-peak ratio and waveform matching are used for accurate cycle identification. The performance of the method is analyzed via simulation. When the signal-to-noise ratio (SNR) ≥ 0 dB and skywave-to-groundwave ratio (SGR) ≤ 23 dB, the success rate of cycle identification is 100%; when SNR ≥ −13 dB and SGR ≤ 23 dB, the success rate exceeds 75%. To verify its practicability, the method was implemented in the eLoran receiver and tested at three test sites within 1000 km using actual signals emitted by an eLoran system. The results show that the method has a high identification probability and can be used in modern eLoran receivers to improve TOA-measurement accuracy.

The Loran-C system is an internationally standardized positioning, navigation, and timing service system. It is the most important backup and supplement for the global navigation satellite system (GNSS). However, the existing Loran-C signal acquisition methods are easily affected by noise and cross-rate interference (CRI). Therefore, this article proposes an envelope delay correlation acquisition method that, when combined with linear digital averaging (LDA) technology, can effectively suppress noise and CRI. The selection of key parameters and the performance of the acquisition method are analyzed through a simulation. When the signal-to-noise ratio (SNR) is −16 dB, the acquisition probability is more than 90% and the acquisition error is less than 1 μs. When the signal-to-interference ratio (SIR) of the CRI is −5 dB, the CRI can also be suppressed and the acquisition error is less than 5 μs. These results show that our acquisition method is accurate. The performance of the method is also verified by actual signals emitted by a Loran-C system. These test results show that our method can reliably detect Loran-C pulse group signals over distances up to 1500 km, even at low SNR. This will enable the modern Loran-C system to be a more reliable backup for the GNSS system.

Loran-C is the most important backup and supplement system for the global navigation satellite system (GNSS). However, existing Loran-C demodulation methods are easily affected by noise and skywave interference (SWI). Therefore, this article proposes a demodulation method based on Loran-C pulse envelope correlation–phase detection (EC–PD), in which EC has two implementation schemes, namely moving average-cross correlation and matched correlation, to reduce the effects of noise and SWI. The mathematical models of the EC, calculation of the signal-to-noise ratio (SNR) gain, and selection of the EC schemes are given. The simulation results show that compared with an existing method, the proposed method has clear advantages: (1) The demodulation SNR threshold under Gaussian channel is only −2 dB, a reduction of 12.5 dB; (2) The probability of the demodulated SNR threshold, being less than zero under the SWI environment, can reach 0.78, a 26-fold increase. The test results show that the average data availability of the proposed method is 3.3 times higher than that of the existing method. Thus, our demodulation method has higher engineering application value. This will improve the performance of the modern Loran-C system, making it a more reliable backup for the GNSS.

Currently, eLoran is the ideal backup and supplement for global navigation satellite systems. The time synchronization accuracy between stations in the eLoran system has improved, providing conditions for eLoran pseudorange positioning. The pseudorange positioning of eLoran is a nonlinear least-squares problem and the location of the eLoran transmitting stations may cause the above problem to be non-convex. This makes the conventional pseudorange positioning al-gorithm strongly depend on the initial value when solving the eLoran pseudorange positioning. We propose a shrink-branch-bound (SBB) algorithm to solve the eLoran pseudorange positioning initialization problem. The algorithm first uses a shrink method to reduce the search space of the position estimator. Then, optimization is performed using a branch and bound algorithm within the shrunk region, where a trust region reflective algorithm is used for the lower bound process. The algorithm can help the receiver to complete the initial positioning without any initial value information. Simulation experiments verify that the algorithm has a success rate of more than 99.5% in solving the initialization problem of eLoran pseudorange positioning, and can be used as an initialization algorithm for pseudorange positioning problems for eLoran or other long-range terrestrial-based radio navigation system.

With the widespread use of the Global Navigation Satellite System (GNSS), its signal vulnerabilities and security issues have become increasingly exposed. Enhanced Long-Range Navigation (eLoran), as a backup system for the GNSS, has gradually attracted widespread attention. This paper investigates and optimizes the eLoran/GNSS combined positioning algorithm. The main research contributions are as follows: (a) Correcting the incorrect application of spatial coordinate transformation relations in the existing literature and re-deriving the eLoran/GNSS combined positioning algorithm based on the Andoyer–Lambert formula. (b) Correcting the eLoran pseudorange positioning equation for altitude in the combined positioning algorithm, compensating for the lack of altitude parameters in eLoran to improve positioning accuracy. (c) Verifying the correctness of the algorithm through simulation analysis, exploring the impact of errors on the algorithm, and evaluating whether the correction of altitude contributes to improving positioning accuracy. (d) Verifying the simulation results through actual measurement analysis.