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.

Satellite clock error is a key factor affecting the positioning accuracy of a global navigation satellite system (GNSS). In this paper, we use a gated recurrent unit (GRU) neural network to construct a satellite clock bias forecasting model for the BDS-3 navigation system. In order to further improve the prediction accuracy and stability of the GRU, this paper proposes a satellite clock bias forecasting model, termed ITSSA-GRU, which combines the improved sparrow search algorithm (SSA) and the GRU, avoiding the problems of GRU’s sensitivity to hyperparameters and its tendency to fall into local optimal solutions. The model improves the initialization population phase of the SSA by introducing iterative chaotic mapping and adopts an iterative update strategy based on t-step optimization to enhance the optimization ability of the SSA. Five models, namely, ITSSA-GRU, SSA-GRU, GRU, LSTM, and GM(1,1), are used to forecast the satellite clock bias data in three different types of orbits of the BDS-3 system: MEO, IGSO, and GEO. The experimental results show that, as compared with the other four models, the ITSSA-GRU model has a stronger generalization ability and forecasting effect in the clock bias forecasting of all three types of satellites. Therefore, the ITSSA-GRU model can provide a new means of improving the accuracy of navigation satellite clock bias forecasting to meet the needs of high-precision positioning.

To address the challenges of handling imprecise building boundary information and reducing false-positive outcomes during the process of detecting building changes in remote sensing images, this paper proposes a Siamese transformer architecture based on a difference module. This method introduces a layered transformer to provide global context modeling capability and multiscale features to better process building boundary information, and a difference module is used to better obtain the difference features of a building before and after a change. The difference features before and after the change are then fused, and the fused difference features are used to generate a change map, which reduces the false-positive problem to a certain extent. Experiments were conducted on two publicly available building change detection datasets, LEVIR-CD and WHU-CD. The F1 scores for LEVIR-CD and WHU-CD reached 89.58% and 84.51%, respectively. The experimental results demonstrate that when utilized for building change detection in remote sensing images, the proposed method exhibits improved robustness and detection performance. Additionally, this method serves as a valuable technical reference for the identification of building damage in remote sensing images.

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.

The National Time Service Center (NTSC), Chinese Academy of Sciences (CAS) has developed a method for Common-View time transfer using Geostationary Earth Orbit (GEO) satellite (GCV) applicable the time and frequency transfer at distant stations. This method is independent of GNSS time and frequency transfer, as well as Two-Way Satellite Time and Frequency Transfer (TWSTFT). A master clock at the time laboratory transmits pseudorandom code signals to a GEO telecommunication satellite, and the time signals is retransmitted by the satellite. Receivers at the time laboratory and user locations coincidentally receive the time signals, and the offsets between the user clocks and the master clock of the time laboratory are determined with high precision if the precise coordinates of the user locations are known. For this technology, only a parabolic antenna with receiver devices and a demodulator are required at each user station, but the coordinates and precise orbits need to be obtained in advance. The key features of GCV include a. continuous coverage of the signal from GEO communication satellites; b. differential observations to reduce the effects of orbit error and the imprecision of the propagation delay model; c. using a Very Small Aperture Terminal (VSAT) to enhance high Signal ratio to noise to obtain precise ranging accuracy and anti-multipath ability; and d. utilizing the C band or Ku band to decrease the impact of the ionosphere. Experiments based on the TWSTFT network of CAS showed that the performance of GCV was at the same level as that of TWSTFT, and the RMS of the residuals of GCV was less than 1.5 ns with respect to TWSTFT.