This article investigates the problem of data-driven formation tracking control under distributed event-triggered mechanisms for underactuated unmanned surface vehicles (USVs) subjected to unmeasurable velocities and model uncertainties. Specifically, to address the issue of communication efficiency among formation members, a frequency-adaptive event-triggered mechanism is proposed. This event-triggered mechanism enables the adjustment of triggering frequency based on the magnitude of errors and accounts for the occasional communication instability in marine environments, while ensuring a minimum inter-event time. Second, a distributed reference state monitor (DRSM) under the novel event-triggered mechanism is developed based on local position information from neighboring USVs. The DRSM enables each USV to estimate its desired state information without relying on global path information. Furthermore, a data-driven dynamic control strategy is proposed based solely on position information. A neural-based data-driven controller is designed to estimate model uncertainties and unknown control input gains using real-time and historical data, without requiring prior knowledge of system parameters. Finally, the effectiveness of the proposed approach is validated through rigorous stability analysis and numerical simulations.

Accurate global ionospheric Total Electron Content (TEC) prediction is critical for high-precision Global Navigation Satellite Systems (GNSS) positioning. Existing deep learning approaches treat spherical ionospheric fields as planar images under equirectangular projection, introducing latitude-dependent geometric distortion and preventing effective modeling of cross-latitude ionospheric coupling. This paper proposes the Cross-Latitude Interactive Spherical Ionospheric Prediction Network (CLISphere): (1) Latitude Isometric Feature Projection decomposing the equirectangular grid into constant-geodesic latitude bands to mitigate projection-induced geometric distortion; (2) a Spherical Feature Interaction Module employing geodesic-distance-biased sparse attention to characterize cross-latitude dependencies associated with meridional ionospheric coupling; and (3) a Spatial Reconstruction Module ensuring spherical prediction continuity. Experiments on International GNSS Service (IGS) Global Ionospheric Maps spanning Solar Cycle 24 show that CLISphere achieves Root Mean Square Error (RMSE) reductions of 10.7%-28.8% during solar maximum and 8.6%-29.9% during solar minimum over five baselines. CLISphere further demonstrates robust performance during geomagnetic storms in both 2015 and 2019. Attention visualizations during storm events reveal preferential weigh-ting at equatorial ionization anomaly crest latitudes (±15° 20°), providing evidence consistent with physically meaningful cross-latitude dependencies.

In constrained environments, such as engineering vehicles or large-scale structures, identifying an optimal open-sky location for a global navigation satellite system (GNSS) antenna installation is often challenging. Consequently, limited satellite visibility and unfavorable geometric distribution can ultimately degrade positioning accuracy and reliability. To address this problem, this article proposes a GNSS antenna array-based visible satellite stitching (VSant ) method for positioning in occlusion environments. The method extends GNSS signal reception from a single-antenna "point" source to an antenna array "area" source, fusing complementary signals from different spatial positions and thereby reconstructing a near-ideal, unobstructed satellite visibility in occlusion environments. Specifically, an attitude information is utilized to compensate for the spatial geometric relationships among antenna arrays in kinematic environments. A comprehensive error analysis is conducted, considering key factors including attitude accuracy, initial calibration error, antenna spacing, and array geometry, to characterize their combined effects on the geometric correction term. The method is validated using both static data from simulated occlusion environments and kinematic data from real-world environments. The results demonstrate that, compared to conventional methods, the VSant method improves the positioning accuracy of single point positioning (SPP) by 21.4%, enhances the positioning accuracy of real-time differential (RTD) by 25.0%, and increases the integer ambiguity fixed rate (AFR) of real-time kinematic (RTK) by 77.1%. These significant improvements highlight the considerable potential of the proposed method for GNSS applications in occlusion environments.

Surface icing on aircraft has long posed a critical threat to flight safety. The risk of stall and loss of control can be markedly elevated due to the degradation of the lift coefficient and the maneuverability of the aircraft, which is caused by the substantial ice accretions. Therefore, the real-time monitoring of ice thickness during flight is essential to enable early warning before safety margins are compromised. In this study, an analytical algorithm for the calculation of Ultrasonic Guided Waves (UGWs) sinusoidal tone burst wave packet evolution is extended to multilayer Transversely Isotropic (TI) waveguides. Its accuracy is confirmed by the comparison of calculation results of transmitted 200 kHz single-cycle Hanning-window-modulated UGW sinusoidal tone burst from the analytical algorithm and the three-dimensional Time Domain Finite Element (TDFE) simulation under various ice dimensions. An experiment is conducted in a cryogenic chamber with PZT-5 wafers for the excitation and sampling of the UGW tone burst in Carbon Fiber Reinforced Plastic (CFRP) laminate under various ice dimensions. The medians and widths of the quarter-peak interval of Mode 2 wave packets are extracted from the experiment results, thus employed together for the dual-parameter wave packet characterization and for the ice thickness prediction under different ice lengths on the basis. The result shows that the average absolute prediction error and the average relative prediction error are 0.10 mm and 18.44% under - 20℃, and 0.12 mm and 21.21% under - 40℃, respectively. Finally, the algorithm is applied to assess the applicability of the excitation signal across varying center frequencies and cycling numbers; an associated optimization criterion is thus proposed.

Carrier-phase differential Global Navigation Satellite System (GNSS) with ambiguity resolution enables all-weather precision landing navigation for aircraft. When deriving differential observations to exploit redundancy in multiple reference receivers like ground-based augmentation system (GBAS), existing studies utilize a common-in-view strategy with a maximum reference receiver to guarantee carrier-phase observation augmentation. However, this method may leave poor satellite geometry for users and, more critically, introduce observation discontinuity, deteriorating ambiguity resolution. To address this issue, we propose a novel observation fusion model – selective array augmentation – to carefully select satellites and fuse observations from multiple reference receivers. An extended Kalman filter (EKF) is utilized to synchronize receiver-specific biases/ambiguity levels, creating a continuity bridge that allows flexibly optimized satellite selection. Subsequently, a performance index accounting for precision constraints is formulated as the selection criterion for observation fusion. Ultimately, we extend the GBAS-like augmentation concept to carrier phase-based landing navigation. Experiments using a testbed demonstrate that the proposed method outperforms the previous methods with a favorable centimeter-level accuracy in both the horizontal and vertical components. Furthermore, the proposed method has the potential to yield enhanced ambiguity resolution performance compared to the traditional methods in constrained environments.

The ionosphere is a major error source in single-frequency GNSS positioning, and Satellite-Based Augmentation Systems (SBAS) mitigate this effect by providing real-time correction information. However, the performance of SBAS under different latitude regions and geomagnetic activity levels still requires further evaluation. Taking EGNOS as an example, this study assesses SBAS-enhanced positioning performance using data from nine IGS stations across Europe. The experiments cover relatively low-, mid-, and high-latitude regions within the EGNOS service area, four representative quarters in 2023, and two disturbed geomagnetic events. Results show: (1) SBAS significantly improves positioning accuracy in all latitude regions, with overall improvement rates ranging from 62.11% to 83.51%. (2) The relatively low-latitude region achieves the largest performance gains, while the mid-latitude region provides the most stable and accurate results. (3) Under disturbed geomagnetic conditions, SBAS still outperforms conventional SPP, but its performance decreases compared with low-Kp periods, with latitude-dependent degradation observed at both MAS1 and SOD3. (4) The integrity analysis further shows that APV II availability reaches 91.85%, whereas CAT I availability decreases to 86.95% under disturbed conditions. Overall, SBAS effectively improves single-frequency positioning accuracy, stability, and integrity, but its performance remains affected by latitude and geomagnetic activity.

Accurate attitude measurement is crucial for small platforms such as unmanned aerial vehicles and portable devices. However, traditional high-precision global navigation satellite system (GNSS) heading determination equipment often fails to meet the strict volume and cost constraints due to their long baselines and high costs. To address this issue, this paper proposes a low-cost and compact GNSS heading determination method based on ultra-short baselines. This method relies solely on carrier phase observations and fully utilizes the geometric characteristics of ultra-short baselines. It directly solves the integer cycle ambiguity using the Rounding method. Based on the theory of hypothesis testing, an analytical relationship model between baseline length and observation noise and the fixed success rate of integer cycle ambiguity is quantitatively established. Experiments show that while significantly reducing the size and hardware cost of the equipment, accurate headings can still be calculated, effectively addressing the application limitations of single-antenna heading determination in static/low-speed scenarios. This research provides an engineering-able system solution for cost and volume-sensitive applications.

Satellite clock offset estimation relies on weighted least squares and Kalman filtering, and its performance is fundamentally constrained by observation quality. Traditional elevation angle stochastic models are widely used due to their simplicity but cannot adequately represent observation noise, especially under abnormal measurement conditions. This study proposes an optimized combined weighting stochastic model integrating elevation angle and carrier-to-noise ratio (C/N0), refined through mutual-information-constrained unsupervised clustering. Mutual information is employed to quantify the dependency between elevation angle and C/N0, followed by K-means clustering and a second mutual-information analysis to evaluate their associations with clustering outcomes. These relationships are incorporated into a unified stochastic weighting strategy that better reflects observation error characteristics. Experimental results for the global positioning system (GPS) and the BeiDou navigation satellite system (BDS) indicate that the standard deviation improvement rates for GPS and BDS reach 18.18% and 11.36%, respectively, while the corresponding root mean square improvement rates are 8.69% and 9.33%. Under conditions involving abnormal observations, the computational time is reduced by 10.98%-34.59%, and 95% of the solutions can be completed within single-iteration, demonstrating that the proposed method exhibits significant advantages in terms of robustness and real-time performance.

Leveraging the characteristics of intelligent reflecting surface (IRS), the data transmission rate and channel capacity of massive MIMO system are significantly improved. In this paper, a model of IRS-aided massive MIMO communication system containing a number of base station antennas, receiving terminal antennas, and an intelligent reflecting surface containing a number of passive reflecting elements is established. For IRS-aided massive MIMO system, in order to resolve channel capacity optimization problem which is a non-convex optimization problem and is difficult to effectively obtain an exact solution, we propose a novel algorithm which is called quantum-inspired water strider algorithm (QWSA). A novel quantum swarm intelligence mechanism derived from quantum theory of computation has been newly designed in this algorithm, namely the quantum water strider adjusts its quantum position not only according to global best of population but also according to the main idea of the quantum water striders within the same group. In IRS-aided massive MIMO system, through joint resource allocation of reflection matrix and transmit covariance matrix, QWSA is applied to solve the optimization problem of channel capacity. Through simulation experiments for the engineering application problem, it is verified that the algorithm significantly improves channel capacity of IRS-aided massive MIMO system.

In an environment with intelligent reflecting surface (IRS), desired signals are enhanced or unwanted signals are suppressed through adaptively adjusting reflecting unit's phase shift. When there is a single-antenna eavesdropper, a single-antenna user received confidential messages from a multi-antenna access point (AP), thus formed our secure wireless communication system. The system initially adopted passive reflecting units, and in order to enhance secure wireless transmission, we propose a new active IRS-assisted system. For the secrecy rate optimization problem in secure wireless communication system, to effectively obtain an exact solution is difficult as it is non-convex property. Aiming at solving the optimization problem above, we newly proposed quantum honey badger algorithm (QHBA). Through jointly designing the reflect beamforming at IRS and transmit beamforming at AP in a legitimate communication link by QHBA, we have maximized the secrecy rate. For both active and passive IRS-assisted secure wireless communication system, we have significantly improved the secrecy rate compared with several traditional swarm intelligence algorithms through simulation results after applying the novel algorithm proposed above.

The continuity of satellite clock corrections in the international GNSS service (IGS) real-time service (RTS) stream is crucial for maintaining high-precision precise point positioning (PPP). When real-time (RT) satellite clock corrections remain unavailable for prolonged periods due to communication outages, the positioning accuracy of the RT-PPP solution will be severely impaired. In this contribution, we propose a hybrid satellite clock offset (SCO) prediction model that integrates singular spectrum analysis (SSA) with a temporal convolutional network (TCN). The SSA is used to decompose the single difference SCO sequence into several signal components with distinct time-frequency characteristics. The TCN is employed to predict each signal component of the single difference SCO sequence independently. The predicted components are reconstructed to sufficiently maintain high-precision SCO prediction for a long interval. The proposed model is compared with the traditional physical, statistical, and neural network models to comprehensively evaluate the performance of SCO prediction. The experimental results show that the SSA-TCN model significantly reduces root mean square error (RMSE) by 67.6

In the practical application of Real-Time Precise Point Positioning (RT-PPP), the outages in receiving spatial state representation (SSR) information due to communication anomalies can result in a decrease or even divergence of the positioning accuracy of RT-PPP. To mitigate the decline in positioning accuracy, we propose a method of INS aiding RT-PPP based on an optimized stochastic model. First, the correlation between SISRE and SSR age was analyzed by using a dataset of 1800 continuous time series. A new stochastic model called clock–orbit degradation (COD) stochastic model was established to match clock–orbit time-varying statistical characteristics. Second, we introduced Inertial Navigation System (INS) enhancement information to optimize the functional model, leveraging its autonomy and high-precision short-term position constraints. Finally, the real-world static and kinematic experiments were designed to verify the proposed method. The static results showed that the RT-PPP positioning accuracy with COD stochastic model is always higher than the traditional fixed equivalent-weight stochastic model at different level SSR outages. Even with SSR interruptions, the positioning accuracy can reach 0.131 m in the horizontal direction and 0.269 m in the 3D direction, representing improvements of 23.2% and 19.0%, respectively. Furthermore, the kinematic results showed that the positioning accuracy of PPP/INS with COD stochastic model had improved by 38.7% in the horizontal direction and 69.9% in the 3D direction at half an hour of SSR age.

Through utilizing intelligent reflecting surface (IRS), high passive beamforming gain can be achieved by utilizing massive low-cost passive components capable of reflecting signals with adjustable phase shifts, and thus significantly improve information transmission efficiency and rate energy balance of simultaneous wireless information and power transfer (SWIPT) system. Aiming at providing service to multiple information decoding receivers (IDRs) and energy harvesting receivers (EHRs), multiple-antenna access point (AP) is assisted by IRS in this paper. To solve the multi-objective optimization of received weighted sum-power of EHRs and sum-rate of IDRs in SWIPT system assisted by IRS technology, we have proposed quantum sand cat swarm optimization algorithm (QSCSO). Derived from quantum-inspired swarm intelligence, a novel mechanism is newly designed by the aid of quantum computing that is quantum sand cats improve the optimal behavior with the help of quantum rotation gate. In our IRS-assisted SWIPT system, to balance between energy harvesting and information transmission, through jointly allocation of reflect phase shifts at IRS and transmit precoders at AP, the sum-rate of IDRs and weighted sum-power of EHRs reception are simultaneously maximized by utilizing the newly proposed QSCSO. Through the newly proposed algorithm, the sum-rate of IDRs and weighted sum-power of EHRs reception are significantly improved in IRS-assisted SWIPT system based on experimental results compared with previous traditional optimization algorithms.

Global navigation satellite system (GNSS) ultra-rapid predicted satellite clock offsets are prerequisite for real-time applications. However, noticeable jump at the boundary between adjacent prediction sessions can cause positioning fluctuation. Unlike observation sessions, the accuracy of prediction sessions decreases over time, making boundary jump elimination methods that treat the jump as an overall error unsuitable for prediction sessions. In this paper, the boundary jump components of prediction session are analyzed and a classification elimination method is proposed. Firstly, we analyze the sources of boundary jump and decompose it into four main components, including timescale difference, initial prediction deviation, initial clock bias, and delay error. After that, elimination methods for each component are proposed based on their specific characteristics. Three schemes for processing boundary jumps are designed: the hourly updated ultra-rapid predicted clock offsets are spliced into 24 h continuous arc using no boundary jump processing (S1), the head-tail concatenation method (S2), and the proposed boundary jump classification elimination method (S5). Experimental results demonstrate that the proposed method reduce the magnitude of boundary jumps and improve predicted clock offset accuracy. Compared with S1 and S2, the clock offset standard deviation (STD), root mean square (RMS) and Range for multi-GNSS in S5 shows significant improvements of (37.8%, 42.5%), (22.0%, 46.2%), and (28.7%, 28.0%), respectively. PPP experimental results demonstrate that the three-dimensional RMS improvements of S5 compared to S1 and S2 are (41.6%, 17.9%) for BDS-only PPP and (25.1%, 8.3%) for multi-GNSS PPP, respectively.

High-precision ionospheric delay is essential for enabling GNSS to achieve accurate applications in fields such as navigation, disaster monitoring, and weather forecasting. This study focuses on the impact of receiver code biases on ionospheric delay extraction using the undifferenced and uncombined Precise Point Positioning (UC-PPP) under multi-GNSS integration. For this purpose, the UC-PPP extended model is proposed, incorporating various receiver code biases through parameter optimization and restructuring to explain ionospheric delay error components. Subsequent comparisons of zero-baseline receiver results, using satellite clock offset products from different analysis centers, confirm that receiver code biases degrade the accuracy of ionospheric delay measurements. Experimental results indicate that GPS provides the most reliable ionospheric delay extraction, followed by BDS, with GLONASS performing the worst. The ionospheric delay extraction is significantly influenced by receiver code biases, with the extent of this effect depending on the magnitude of the receiver code biases in the satellite clock offset products and receiver. Significant differences in ionospheric delay accuracy are observed between receivers of the same and different types under zero-baseline. The maximum differences in ionospheric delay accuracy for GPS, GLONASS, and BDS are 0.03 total electron content unit (TECU), 0.65 TECU, and 0.13 TECU (15%, 70.65%, and 26%), respectively.

Cycle slip detection and repair are of great significance in achieving high-precision positioning with carrier phase observations. Because incorrect cycle slip detection and repair will adversely affect the positioning performance, a failure controllable cycle slip detection and repair method is proposed. The method constructs a geometry-free combination by using high-rate Doppler observations and eliminating the integer terms as statistics. Then, the narrowing domain is adaptively determined based on the quality of observations to tightly control the failure probability of cycle slip detection and repair. The factors influencing the quality of observation are systematically analyzed, and a scheme for determining the standard deviation through Gaussian fitting of historical data is presented. To evaluate the performance of the proposed method, the data collected by the Ublox M8T receiver is utilized with simulation of one-cycle slip. The results indicate that when Pfa, req and Pmd, req are set to 10-3, the traditional method fails to meet the requirements. However, for the proposed method, Pfa reaches 0.014% and Pmd reaches 0.006%, both of which meet the user requirements. Furthermore, Psuccess of the proposed method reaches 99.991%, exceeding that of the traditional method by 0.429%.

Real-time Precise Point Positioning (PPP) has emerged as a critical technology for delivering absolute highprecision positioning in maritime precision operations. To address stringent integrity requirements in safety-critical maritime applications, PPP integrity monitoring must establish rigorously guaranteed protection levels through systematic error bounding. However, the complex risk sources in marine environment, such as significant non-Gaussian multipath errors, make it difficult to build threat model. This limitation causes existing integrity frameworks to fall short of the required performance. Therefore, a solution separation-based integrity monitoring method for real-time PPP in maritime precise operating environment is proposed. Through failure mode effect analysis and overbounding for non-Gaussian errors, a suitable threat model is constructed. Both the near offshore and ocean experiments with the integrity requirement from international maritime organization are used to test the effectiveness of proposed method. The results indicate that the proposed method can produce adequate protection levels with given integrity risk of 1 x 10(-5)/3 h. It can achieve better availability and effectively suppress misleading information.

Real-time precise point positioning (RT-PPP) relies on real-time service (RTS) as satellite orbit and clock corrections for users to achieve decimeter or centimeter-level positioning. Although the RTS is nearly always with high accuracy, it will inevitably have abnormal values or accuracy degradation. The faults from satellite orbit and clock corrections must be detected to provide a reliable positioning performance. In this contribution, a state-domain-based integrity monitoring method of satellite orbit and clock corrections for RT-PPP is proposed. The prediction models for satellite orbit and clock correction parameters are constructed by analyzing the daily variation statistical characteristics of correction parameters in the state domain. After compensation for nonideal error distribution by overbounding, the detection statistics and minimum detection errors are established based on the corrected precision satellite orbit and clock comprehensive errors, which aim to monitor the quality of satellite corrections. The experiment is used to test the false alarm and missed detection performance of the proposed method. The results confirm that the abnormal satellite orbit and clock corrections can be detected accurately with a given missed detection rate of 1x10(-3 )and false alarm rate of 1x10(-5 ). Moreover, using the proposed monitoring method, the RT-PPP results after excluding abnormal satellite orbit and clock corrections can be improved by an average of 6.51%, 13.94%, and 8.14% in the east, north, and up directions, respectively.

The narrow lane (NL) ambiguity resolution (AR) is the key to cascade extra wide lane-wide lane (WL)-NL precise point positioning (PPP)-AR to achieve rapid maritime centimeter-level positioning. However, the increased ratio of noise to wavelength and combined noise in ionospheric-free (IF) combinations will reduce the success rate of fixing the NL ambiguity. Additionally, more significant unmodeled errors and observation noise in the pseudo-range are likely to degrade WL AR performance, which reduces the fixing success rate of NL ambiguities. We proposed an advanced BDS-3 quad-frequency IF combination PPP-AR to address these challenges. First, a new quad-frequency IF combination NL observation is developed to reduce the combined noise, thereby improving the fixing success rate. Second, a phase-only model is constructed to calculate float ambiguities of WL and NL simultaneously. The phase-only model avoids the influence of unmodeled errors and observation noise in the pseudo-range, thereby improving the fixing success rate of WL ambiguities. Finally, the fixing success rate of NL ambiguities increases due to lower combined noise and higher fixing success rate of WL ambiguities. To verify the performance of the proposed method, the experiments were conducted using data from 11 multi-GNSS experiment static stations and an ocean kinematic platform. Compared to the WL AR using pseudo-range observations, the fixing rate of the proposed method improved by 2.9%. Compared to the existing IF NL combination, the multi-station experiment demonstrated an increase of approximately 7.3% in the average fixing rate. The convergence time improved by approximately 22.1%. Furthermore, the ocean platform experiment revealed that the average convergence time was reduced to 2.45 min, which improved by about 45.5%.

Real-time service (RTS) products are an important guarantee for real-time precise point positioning (RT-PPP), and the RTS outages caused by loss of network connection are a concern. In this paper, a multivariate CNN-LSTM model is proposed for short-term BDS satellite clock offset prediction during the discontinuity in receiving RTS clock offsets, which utilizes the superior feature of convolution neural network (CNN) and long short-term memory (LSTM) for simultaneous prediction of multiple satellite clock offsets by considering the inter-satellite correlation. First, the correlation between satellite clock offsets was analyzed to identify satellites suitable for parallel prediction. Then, to preserve the sequential structure of the features extracted from multiple parallel satellite clock offsets, remove the pooling layer of traditional CNN, and use the convolution layer to learn the relationships and dependencies between clock offsets of different satellites and the LSTM layer to model the temporal dependencies in satellite clock offsets. The experiment results show that the computational efficiency of the proposed model is significantly better than that of autoregressive integrated moving average (ARIMA), wavelet neural network (WNN), and LSTM models. Compared with the linear polynomial (LP), quadratic polynomial model (QP), ARIMA, WNN, and the LSTM models, the prediction accuracy of the multivariate CNN-LSTM model for 5 min, 15 min, 30 min, and 1 h is improved by approximately (84.0, 76.6, 1.5, 8.3, 8.3)