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Specific emitter identification (SEI), which classifies radio transmitters by extracting hardware-intrinsic radio frequency fingerprints (RFFs), faces critical challenges in noise robustness, generalization under limited training data and class imbalance. To address these limitations, we propose adaptive deep ensemble learning (ADEL)—a framework that integrates heterogeneous neural networks including convolutional neural networks (CNN), multilayer perception (MLP) and transformer for hierarchical feature extraction. Crucially, ADEL also adopts adaptive weighted predictions of the three base classifiers based on reconstruction errors and hybrid losses for robust classification. The methodology employs (1) three heterogeneous neural networks for robust feature extraction; (2) the hybrid losses refine feature space structure and preserve feature integrity for better feature generalization; and (3) collaborative decision-making via adaptive weighted reconstruction errors of the base learners for precise inference. Extensive experiments are performed to validate the effectiveness of ADEL. The results indicate that the proposed method significantly outperforms other competing methods. ADEL establishes a new SEI paradigm through robust feature extraction and adaptive decision integrity, enabling potential deployment in space target identification and situational awareness under limited training samples and imbalanced classes conditions.

In order to improve the interoperability within the Global Navigation Satellite System (GNSS), the International Committee on Global Navigation Satellite Systems published a joint statement in December 2019 that stated that all GNSS providers agree to monitor and broadcast the time offsets between each system timescale and the Universal Time Coordinated (UTC) or the rapid realization of UTC (UTCr). This commitment requires the study of precise prediction models for system time offsets. The prediction model of system time offsets is different from that of the atomic clock because of the control of the system timescale. The offsets between the system time of the Beidou Satellite Navigation System-3 (BDS-3) and the National Time Service Center (NTSC), called [UTC(NTSC)-BDT], have two main periods of 12 h and 24 h, according to the Fast Fourier Transform analysis. The rescaled range (R/S) analysis demonstrates that it has long memory, making it a fractal time series with a memory period of about 10.4 h. While using the fractal interpolation method to predict the [UTC(NTSC)-BDT] series, we found that the prediction error reaches its minimum value if adding disturbance on the estimated endpoint of the forecasted interval. After verifying the correlation between the estimated endpoints with the minimal interpolation error and minimal prediction error and proving the existence and uniqueness of the estimated endpoint with the minimal interpolation error, we established the endpoint-based optimal fractal interpolation prediction method. The experimental results indicate that the average prediction accuracy of the proposed prediction model is improved by 57.90% and 39.26% compared to that of a quadratic model and standard fractal prediction model, respectively. The accuracy analysis results of numerical tests indicate that the proposed prediction model can restrain the divergence of prediction error. Finally, we transform the [UTC(NTSC)-BDT] into [UTCr -BDT] using the [UTCr-UTC(NTSC)] published by the Bureau International des Poids et Mesures (BIPM) to meet the requirement of GNSS interoperability. The prediction accuracies of daily [UTCr-BDT] using the proposed prediction model are no more than 1.5 ns with uncertainty about 6 ns.

Compared to conventional spoofing, emerging spoofing attacks pose a heightened threat to security applications within the global navigation satellite system (GNSS) due to their subtly designed signal structures. In response, a novel spoofing detection method entitled aggregated correlation residue likelihood analysis (A-CoRLiAn) is proposed in this study. Requiring only the addition of a pair of supplementary correlators, A-CoRLiAn harnesses correlation residues to formulate a likelihood metric, subsequently aggregating weighted decisions from all tracked satellites to ascertain the presence of spoofing. Evaluated under six diverse spoofing scenarios (including emerging challenges) in the Texas Spoofing Test Battery (TEXBAT) via Monte Carlo simulations, A-CoRLiAn yields a detection rate of 99.71%, demonstrating sensitivity, robustness, autonomy, and a lightweight architecture conducive to real-time implementation against spoofing threats.

The Quasi-Zenith Satellite System (QZSS) plays a pivotal role in providing vital navigation, positioning, timing, and signal authentication services, particularly through its L5 signal. Despite its importance, research on the performance of the L5 signal remains relatively limited. This study presents an empirical analysis of the L5 signal, identifying the distinct amplitude and phase distortion phenomena within its constellation diagram. Simulation methods are employed to replicate these observed anomalies, revealing that the L5 signal is significantly impacted by in-band inter-signal interference and the multipath effect at the satellite end of the star. A quantitative analysis is performed to investigate the underlying causes of these distortions, offering a deeper understanding of the factors contributing to the observed signal irregularities. The findings provide essential data and theoretical insights, contributing to the optimization of the QZSS signal quality and performance.

Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas).

Traction spoofing is an important component of Global Navigation Satellite System (GNSS) intermediate attacks, and the traction scheme directly determines the concealment of spoofing. However, spoofing via conventional traction strategies can be easily detected using Time of Arrival (TOA) and power detection. Based on a BPSK-modulated signal, a novel traction strategy using traction code is proposed to suppress part of the authentication signal and form an ideal correlation peak. This strategy was modeled and simulated to verify its theoretical feasibility. Effective spoofing data were generated based on the signal generation software to verify the spoofing effect with the reception of the software receiver. It can be inferred that no significant distortion occurred throughout the traction process, and the value range of the traction speed was expanded. The received results in different scenarios demonstrated that the observations’ Root-Mean-Square Error (RMSE) percentage change in the proposed strategy is significantly better than those of conventional strategies. A Ratio Test was also performed, verifying that the strategy can bypass Signal Quality Monitoring (SQM) detection. Meanwhile, the proposed strategy remained effective when the C/N0 increased to 60 dBHz. In summary, the proposed strategy exhibits destructiveness, concealment, and adaptability on the battlefield.

A distinctive feature of modern Global Navigation Satellite System (GNSS) signals is that they transmit multiple signal components at the same carrier frequencies. The idea of joints across the signal channels from the same carrier frequency and even across different frequencies has been presented in many studies for tracking purposes. Carrier joint tracking is required on the premise that the frequency and phase relationship between signals are nominal values, and the bias of carrier characteristics between signals is drowned in noise as the signal reaches the ground, which requires high-gain receiving equipment to restore the original signal. The space signal-quality monitoring and evaluation system built by the National Timing Center of the Chinese Academy of Sciences is based on a 40 m dish antenna, which can automatically track a single satellite and achieve a high-fidelity reception of navigation signals to a certain extent, realizing fine signal quality monitoring (SQM) of GNSS satellites. Based on this platform, we discuss four types of time distributions of the combined signals among different signal components and provide a method to estimate the carrier characteristic bias between GNSS signals. We derived the correction method of carrier characteristic bias in the joint reception by the joint tracking mathematical model. Under the conditions of narrow correlation and unobstructed case, the carrier characteristic deviation does not vary significantly with the correlator interval and the satellite elevation angle. Based on the results of stability analysis, it is recommended that the receivers should update the carrier frequency bias correction number of the intra-frequency signal and carrier phase bias correction number of the intra-frequency signal monthly. The carrier phase deviation correction number of the inter-frequency signal is performed daily. The measured data from satellites show that the phase accumulation error of the joint tracking carrier loop can be eliminated to achieve long-term stable tracking after frequency bias correction. After the carrier phase bias correction, the joint positioning accuracy of the B2a and B2b signals was improved by 0.81%, and those of the B1C, L1C, E1C, and B2a signals were improved by 0.35%, 0.04%, 0.20%, and 0.11%, respectively. The positioning accuracy improvement effect of inter-frequency signals was greater than that of intra-frequency signals after carrier phase correction.

The design of a modern Global Navigation Satellite System (GNSS) has been exceptionally valued by the military and civilians of various countries. The inclusion of the pilot channel in addition to the navigation data channel is considered one of the major changes in GNSS modernization. Schemes of an equal weight combination (1:1 combination) and power ratio combination for data and pilot are primarily adopted by traditional receivers. With the emergence of the new data and pilot modulation signals with unequal power, such as L1C at Global Positioning System (GPS) L1 frequency and B1C at BeiDou Navigation Satellite System (BDS) B1 frequency, the traditional combination coefficient cannot achieve optimal reception performance. Considering the influence of the combination coefficient on the reception performance, the optimal coefficient of the correlator joint is estimated in this paper. The entire architecture of the data/pilot correlator joint tracking and positioning with unequal power is given. Based on the equivalence principle of the correlator joint and the discriminator joint, the optimal coefficient of the carrier loop is determined. A mathematical model of joint code tracking accuracy is established, and the optimal coefficient of the code loop is determined. The real-life satellite signal and simulation results show that the amplitude–ratio combined scheme is the best for receiving of correlator joints, followed by the power–ratio combination scheme and, finally, the 1:1 combination scheme. It is worth mentioning that the positioning accuracy of the amplitude–ratio combination is improved by 2% compared to the 1:1 combination, and by 1.3% compared to the power–ratio combination for B1C signal. The positioning accuracy of the amplitude–ratio combination is improved by 2.37% compared to the 1:1 combination, and by 1.6% compared to the power–ratio combination for L1C signal. The conclusions of this paper are validated for the traditional data/pilot with an equal power allocation. The techniques and test results provide technical support for GNSS high-precision-user receivers.

The standard products of the International Laser Ranging Service (ILRS) are mainly based on the two laser geodynamics satellites (LAGEOS) due to the sparse observations of the Etalon satellites. With improvements in the ability to track high-altitude satellites, ILRS conducted a 3-month Etalon tracking campaign. In this paper, we study the contribution of more Etalon observations in the new observation scenario to weekly ILRS products, such as station coordinates, Earth rotation parameters (ERPs) and satellite orbit. We compare the ILRS products estimated from LAGEOS-only solutions and LAGEOS+Etalon solutions. In the new observation scenario of 2019, the numbers of observations of Etalon satellites are 1.4 and 1.7 times larger than those in 2018. It is shown that the quality of station coordinates, and the satellite orbit of LAGESOS satellites are only slightly affected by the increase in Etalon observations of the campaign. However, for station 1868, which is dedicated to high-altitude satellites, the root mean square (RMS) values of the residuals in the N, E, and U components are improved by 3.1 cm, 2.1 cm and 2.3 cm, respectively. The internal precision of orbit for Etalon-1/2 satellites in tangle and normal directions are improved by 1.5 cm and 2.9 cm, respectively. Most remarkably, the standard deviations for Xp, Yp and LOD can be improved by 6.9%, 14.3% and 5.1%, respectively, compared with the International Earth Rotation System (IERS)-14-C04 series. With our research, the ILRS could increase efforts on Etalon satellite tracking without affecting the routine observations of LAGEOS satellites.

China is promoting the construction of an integrated positioning, navigation, and timing (PNT) systems with the BeiDou Navigation Satellite System (BDS) as its core. To expand the positioning coverage area and improve the positioning performance by taking advantage of device-to-device (D2D) and self-organizing network (SON) technology, a BDS/SON integrated positioning system is proposed for the fifth-generation (5G) networking environment. This system relies on a combination of time-of-arrival (TOA) and BeiDou pseudo-range measurements to effectively supplement BeiDou signal blind spots, expand the positioning coverage area, and realize higher precision in continuous navigation and positioning. By establishing the system state model, and addressing the single-system positioning divergence and insufficient accuracy, a robust adaptive fading filtering (RAF) algorithm based on the prediction residual is proposed to suppress gross errors and filtering divergence in order to improve the stability and accuracy of the positioning results. Subsequently, a federated Kalman filtering (FKF) algorithm operating in fusion-feedback mode is developed to centrally process the positioning information of the combined system. Considering that the prediction error can reflect the magnitude of the model error, an adaptive information distribution coefficient is introduced to further improve the filtering performance. Actual measurement and significance test results show that by integrating BDS and SON positioning data, the proposed algorithm realizes robust, reliable, and continuous high precision location services with anti-interference capabilities and good universality. It is applicable in scenarios involving unmanned aerial vehicles (UAVs), autonomous driving, military, public safety and other contexts and can even realize indoor positioning and other regional positioning tasks.