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Specific emitter identification (SEI) is a professional technology to recognize different emitters by measuring the unique features of received signals. It has been widely used in both civilian and military fields. Recently, many SEI methods based on deep learning have been proposed, most of which assume that the training set and testing set have the same data distribution. However, in reality, the testing set is generally used later than the training set and lacks labels. The long time span may change the signal transmission environment and fingerprint features. These changes result in considerable differences in data distribution between the training and testing sets, thereby affecting the recognition and prediction abilities of the model. Therefore, the existing works cannot achieve satisfactory results for a long time span SEI. To address this challenge and obtain stable fingerprints, we transform the long time span SEI problem into a domain adaptive problem and propose an unsupervised domain adaptive method called LTS-SEI. Noteworthily, LTS-SEI uses a multilayer convolutional feature extractor to learn feature knowledge and confronts a domain discriminator to generate domain-invariant shallow fingerprints. The classifier of LTS-SEI applies feature matching to source domain samples and target domain samples to achieve the domain alignment of deep fingerprints. The classifier further reduces the intraclass diversity of deep features to alleviate the misclassification problem of edge samples in the target domain. To confirm the effectiveness and reliability of LTS-SEI, we collect multiple sets of real satellite navigation signals using two antennas with 13 m- and 40 m-large apertures, respectively, and construct two available datasets. Numerous experiments demonstrate that LTS–SEI can considerably increase the recognition accuracy of the long time span SEI and is superior to the other existing methods. [ABSTRACT FROM AUTHOR]

The Sarez Dam, currently recognized as the world's highest natural dam, holds immense economic significance, necessitating the reliable monitoring and early detection of potential deformations. This study utilizes the Beidou high-precision deformation monitoring system for the dam. Employing baseline network solutions, precise point positioning, and real-time kinematic positioning, the monitoring data from October 2021 to March 2023 were meticulously processed and comparatively analyzed. The results reveal varied degrees of displacement deformation at all sites, with horizontal deformation towards the lake center and vertical deformation showing subsidence. The three-dimensional vector average displacement is 29.1 mm, with an average monthly rate of 4.3 mm/month, and the maximum deformation is 41.4 mm. The cause of the horizontal deformation towards the lake center may be a slow collapse of the coastal soil and rock towards the lake center. Additionally, the monitoring detected a 7.2-magnitude earthquake on 23 February 2023, 52 km from the dam, causing an average displacement of 22.5 mm towards the south, which is 20 times the monthly deformation rate. In summary, deformation is present within the dam monitoring area, with a relatively stable deformation rate, warranting continued tracking and monitoring. Furthermore, earthquakes significantly impact dam deformation, necessitating heightened attention to the potential for seismic-induced dam damage in the future. [ABSTRACT FROM AUTHOR]

Accuracy and reliability are determining factors when selecting where to place a global navigation satellite system (GNSS) receiver for landslide monitoring and early warning in real time. Previous studies have revealed that surface occlusion models (SOMs) are beneficial to reduce the multipath error of satellite observation when the satellite line of sight is near the ground and improve the accuracy and reliability of GNSS monitoring. This paper investigates fitting functions to determine appropriate SOMs for GNSS stations located in different spatial environments. Based on their similarity to terrain patterns, Gaussian, Fourier, and sum of sine functions were chosen to build SOMs for the rover sites with different occlusions; the performance of fitting accuracy and application on the positioning were analyzed. The results indicate that the three functions present a similar performance considering the goodness of fit, but the Fourier and sum of sine functions can significantly improve the efficacy of SOMs and enhance performance in positioning for landslide monitoring. Therefore, these two functions can effectively support accurate and reliable GNSS landslide monitoring in real time. [ABSTRACT FROM AUTHOR]

The Beidou-3 system (BDS-3) satellites are deployed, equipped with an inter-satellite link (ISL) payload, so that the satellite–satellite or satellite-ground time synchronization is available. The satellite-ground link (GSL) can be used in conjunction with the ISL if proper attention is paid to all corrections and the biases are fitted. We execute methods to synchronize the clocks of BDS-3 satellites to an accurate reference clock on the ground, by estimating relative clock offsets obtained via Ka-band GSL observations. We solve for the relative biases using the closure properties of the redundant biases and the Ka-band (Ka-mode) satellite-ground two-way data. We find the biases, relative to the GSL hardware delay, are relatively stable in the short term. This makes it feasible to estimate them as a constant every few hours, and the standard deviation of the hardware delay estimations within 24 h is slightly less than 0.7 ns. The frequency stability of Ka-mode estimated clock offset shows a negative short-term performance because of the dominant effect of the white phase noise in the relative clock offsets from the Ka-mode, but the long-term performance seems to be improved to a higher level. The time synchronization accuracy of Ka-mode is proven to be better than 0.3 ns (RMS), whereas the accuracy for the high-orbit satellite is slightly worse. [ABSTRACT FROM AUTHOR]

In this study, 22 maneuvering events of C59 and C60 geostationary equatorial orbit (GEO) satellites of the BeiDou global navigation satellite system (BDS-3) in 2021 were detected and analyzed based on a time-differenced carrier phase velocity measurement algorithm combined with a broadcast ephemeris and station data. We set different empirical thresholds to analyze the effect of threshold selection on the maneuvering detection sensitivity. The sensitivity and consistency of this algorithm for GEO satellite maneuvering detection were analyzed by combining BDS-3 with other Global navigation satellite systems (GNSS) and selecting different geographic stations. The results demonstrate that changing the threshold significantly affects the maneuvering detection of C60, with the optimal maneuvering thresholds of C59 and C60 being 0.045 and 0.02, respectively. A combination of BDS-3 and GPS with equal weights was identified to be most suitable for maneuvering detection of GEO satellites. For any specific maneuver, the difference between the maneuvering time detection of C59 by stations at different geographical locations was no more than 1 min. During the maneuvering detection of C60, the start time of maneuvering detected by African stations was 4.26–8.61 min earlier than that detected by the Chinese and Australian stations, and the end time of maneuvering detected by the African stations was consistent with that detected by the Chinese and Australian stations. [ABSTRACT FROM AUTHOR]

Traditional PPP-RTK algorithm research has focused on users in a reference station network. However, if a proper PPP-RTK user algorithm can be designed for out-of-network users to achieve accurate and rapid positioning, the effective service area of existing reference station networks can be markedly expanded. To address the problem of extrapolating atmospheric corrections for out-of-network users, this study presents a single-reference-station (SRS) PPP-RTK model to address atmospheric delay corrections, by ensuring that only corrections from the closest reference station are used. Several numerical experiments were designed and conducted to compare the performance of the presented model and a widely used interpolation model. The extrapolation error distribution, mean and the maximum of absolute values of errors, and standard deviation of errors were selected as indicators. The results show that the SRS PPP-RTK model is significantly more accurate than the interpolation model. Thus, for out-of-network users, we suggest that the presented SRS PPP-RTK model should be used, and the residual atmospheric delays are needed to be estimated. [ABSTRACT FROM AUTHOR]