Considering the wide application and critical importance of satellite navigation systems, aiming at the requirements for precision and reliability of the next-generation BeiDou satellite navigation system, this study investigates the current development status of integrity technology from four dimensions: basic integrity of satellite navigation systems, integrity of satellite-based augmentation systems (SBAS), advanced receiver autonomous integrity monitoring (ARAIM), and precise point positioning (PPP)integrity, and compares the technical difficulties of each. Finally, it explores the development trends of future integrity technologies. This study is significant for the design and construction of the integrity system of the next-generation BeiDou satellite navigation system.

In order to improve the orbit determination accuracy of BDS satellites and solve the problem of limited construction of BDS ground stations, this paper proposes a solution using a LEO constellation as a space-based monitoring station. Two indicators were proposed to evaluate the monitoring performance of LEO constellations: Monitoring coverage factor and Satellite Position Dilution of Precision(SPDOP), and different configuration parameters of LEO constellations were optimized, including orbital altitude, number of orbital planes, orbital inclination angle, and number of satellites. An optimal LEO constellation was designed using the BDS-3 satellite as the monitoring object, and the monitoring capability of the LEO constellation was verified based on 30 tracking stations of the iGMAS system. The results show that the monitoring performance of the designed LEO constellation is superior to that of the iGMAS system with 30 tracking stations. The LEO constellation can achieve at least 6-fold of coverage over the entire arc of the BDS satellite with the minimum number of LEO satellites, with a good and stable geometric layout, which helps to improve the orbit determination accuracy of the BDS system and provide high-quality observation data for monitoring and evaluating the operating status and service performance of the BDS system.

To address spectrum scarcity, complex airspace, and moving obstacles in large-scale low-altitude operations, a UAV decision framework that couples communication, control, surveillance, and trajectory are proposed. Two performance maps, Information Performance Map (IPM) and Surveillance Performance Map (SPM), are built to quantify control-link availability and radar reliability. A cumulative outage constraint ensures both flyability and controllability while three-dimensional point-cloud data are exploited to maximize the air-to-ground rate. A DQN (Deep Q-Network)-based algorithm is then introduced: point-cloud and CNN(Convolutional Neural Network) features are jointly processed to select the next waypoint and vehicle access in a discrete action space, with experience replay and a target network stabilizing training. After 8 000 training episodes, the UAV cruises efficiently through areas of robust control and radar coverage while avoiding blind spots, as rewards converge and losses stabilize. The proposed method offers a scalable solution that balances spectrum efficiency, safety, and regulation.

Given the vulnerability of satellite signals to interference, the research on anti-jamming algorithms based on array antennas becomes crucial to ensure the reliable positioning accuracy of GNSS receivers. However, the existing variable step-size power inversion algorithms rely on the single regulation mechanism of instantaneous energy, which have insufficient stability in the dynamic interference scenarios. This paper proposes a variable step-size anti-jamming algorithm modified by power change rate, and this method adds the power change rate to correct the step-size variation based on the original variable step-size. Through the dual adjustment mechanism of input power normalization and output power change rate correction, the proposed algorithm is promoted to restore quickly the stable convergence state after an abrupt power change. This method effectively mitigates the violent oscillation of the weights caused by rapid power changes, improving both the convergence speed and robustness of the algorithm in the dynamic interference environment. Simulation experiments show that the proposed algorithm achieves faster convergence speed and deeper null depths than a single adjustment mechanism relying solely on instantaneous energy. Moreover, it can effectively cope with the impact of abrupt changes in signal power on interference suppression, thereby reducing interference signal power.

Inertial/Global Navigation Satellite System (GNSS) integrated navigation has been widely applied in various mobile platforms such as unmanned aerial vehicles (UAVs). However, during GNSS signal outages, INS errors accumulate rapidly, severely degrading navigation accuracy. Existing research primarily focuses on horizontal two-dimensional error modeling while neglecting the dynamic characteristics in the vertical (altitude) direction, limiting its practical application in three-dimensional space. To address this issue, this paper proposes a dual-branch neural network model for three-dimensional navigation, which simultaneously models position increments in the longitude, latitude, and altitude directions to cater to the demands of dynamic navigation in 3D space. The model adopts a decoupled dual-branch structure built with LSTM and GRU networks, designing separate modeling paths for the horizontal and vertical components. A convolutional neural network (CNN) is further incorporated into the main branch to enhance temporal feature extraction. Experimental results demonstrate that the proposed network significantly improves three-dimensional navigation accuracy. Compared with conventional positioning methods, it reduces the root mean square error(RMSE) along the east, north, and up axes by 97.8 %, 97.9 %, and 26.2 %, respectively, demonstrating its strong potential for practical deployment.

In autonomous driving within the Internet of Vehicles (IoV), positioning accuracy is key to stable operation. However, standalone navigation systems such as satellite navigation and inertial navigation cannot fully ensure continuous high-precision positioning. Therefore, achieving high-precision positioning through information collaboration between vehicles has become the main approach. This paper proposes a neural network-based large-scale cooperative vehicle positioning method. Aiming at the characteristic of vehicles freely gathering and dispersing during driving, principal component analysis is introduced to process navigation information and reduce computational complexity. Furthermore, the Fireworks Neural Network method is used to rapidly fuse navigation information in the IoV, ensuring positioning accuracy and stability during vehicle operation. Compared with existing cooperative positioning methods, experimental results show that the proposed method has faster convergence and better positioning stability.

The current navigation system relies heavily on GNSS system, which makes the users unable to work in the denied environment. This paper analyzes the navigation and position technology by low orbit satellite, based on satellite receiver's Doppler measurement, this technology can be used to realize the positioning of user on earth. On the condition of orbit altitude 800 km, observing arc segment 8 min, measuring error of Doppler 0.01 Hz, the simulating result is that the user's positioning error is 1 000 m. In the Global Positioning System denied environment, the findings of this study would offer an alternative positioning approach for specific user groups.

At present, GNSS-IR sea surface height retrieval technology relies primarily on traditional geodetic GNSS receivers. However, due to their high cost and portability limitations, these devices struggle to meet the demands for low-cost and high-precision applications, which restricts the widespread adoption of this technology. To address this, our study utilizes smartphones for GNSS-IR sea surface height inversion experiments. By employing robust estimation techniques, we integrated multi-system and multi-frequency data to enhance the accuracy of the sea surface height inversion. The results indicate that the smartphone's overall retrieval accuracy is approximately 21 cm, with an average correlation coefficient of 0.96, performance comparable to that of traditional GNSS receivers. The multi-system multi-frequency data fusion further improved the sea surface height retrieval accuracy to 6.8 cm, with a correlation coefficient of 0.996. Compared to the initial retrieval results, this approach significantly enhanced both the temporal resolution and the accuracy of sea surface height retrieval. After smoothing, the accuracy of the fused retrieval results reached 6.1 cm. These findings demonstrate that smartphones can achieve centimeterlevel accuracy in sea surface height retrieval, making them a viable alternative to traditional GNSS receivers for GNSS-IR applications.

To counteract the influence of the Earth's non-spherical perturbation, Geostationary Earth Orbit (GEO) satellites must periodically execute longitude drift control to maintain their position in the east-west direction. East-west station-keeping is typically achieved via pulse ignition. During this process for geostationary orbit satellites, continuous telecommand must be sent, severely restricting payload applications closely coupled with satellite telecommand. This paper devises a decentralized control approach for the east-west station-keeping of GEO satellites, constructs a mathematical model, formulates control strategies and implementation details, and validates the control effectiveness using two distinct types of in-orbit satellites. This method efficiently exploits fragmented resources during payload task intervals, enhancing the availability of satellite tracking, telemetry, and telecontrol command resources.

With the rapid development of smart vehicles and autonomous driving technology, high-precision 3D navigation has emerged as a crucial supporting technology. However, the limitations of the BeiDou Navigation Satellite System (BDS) in terms of accurate elevation positioning and complex, variable highway traffic scenarios have posed constraints on autonomous and intelligent driving systems. This paper proposes a BDS+5G integrated positioning model based on adaptive Kalman filtering technology, aiming to address the decline in positioning accuracy caused by signal occlusion, signal loss, and multipath effects in complex highway traffic environments. By constructing an integrated positioning vector equation and introducing innovation vectors and robust factors, the model achieves adaptive suppression of measurement noise, thereby enhancing positioning accuracy. Field tests conducted at the Liuxia Hub on the Hangzhou Beltway Expressway, coupled with superimposed validation using 3D high-precision laser point cloud maps, demonstrate that this model can significantly improve positioning performance in complex traffic environments, with an elevation positioning error of less than 0.2 m, capable of supporting 3D lane-level navigation. This validates the application potential of BDS+5G hybrid positioning technology in the field of smart vehicles and autonomous driving.

Traditional inertial-satellite integrated navigation relies on satellite navigation to provide high-precision positioning results for position correction, which is difficult to adapt to the environment of strong suppression interference. To address this issue, this paper proposes an inertial navigation correction method based on interference direction-of-arrival (DOA) estimation, which can provide satellite-independent positioning correction information in strong jamming environments. The algorithm first estimates the interference direction-of-arrival (DOA) using compressed sensing-based direction finding, then combines this with the shortterm high-precision vehicle trajectory provided by inertial navigation to localize low-dynamic or stationary interference sources. Subsequently, it utilizes the estimated interference source positions and DOA information to inversely determine the aircraft’s position, ultimately providing the inertial navigation system with a satellite-independent correction reference. It is suitable for extreme suppression interference confrontation environment where satellite navigation cannot work for a long time. The main innovation is that a robust positioning method based on the position estimation of the interference source, which provides a extra positioning information source other than satellite navigation for inertial navigation in the application scenario where satellite navigation fails due to interference, and the corrected positioning information source does not need to add additional hardware sensors. The simulation results show that the algorithm can provide stable positioning for inertial navigation in the environment of four static strongly suppressed interference sources, and meet the navigation and positioning requirements in the environment of strong electromagnetic interference.

To evaluate the Doppler positioning performance of Low Earth Orbit (LEO) satellites, this paper analyzes the related errors and positioning performance of single-LEO navigation test satellite. Furthermore, the worldwide constellation Doppler positioning performance is analyzed in conjunction with low-orbit constellation simulation extrapolations. The results show that: ① The Doppler measurement error accuracy is at the decimetre level, which is more than one order of magnitude greater than the other error terms. Furthermore, the Doppler User Equivalent Range Rate Error (UERRE) accuracy of the comprehensive related error term is better than 0.27 m/s; ② The single-satellite Doppler positioning 3D error converges to 200 m in approximately eight minutes, with a post-convergence positioning accuracy of approximately 85 m. Furthermore, the single-star Doppler-equivalent PDOP eventually converges to around 200; ③ When the cut-off altitude angle is 10° or less and the cumulative observation time is 8 min or more, the global average of the constellation Doppler equivalent PDOP is superior to 28.8 m, the RMS is superior to 58.8 m, and better than 156.1 m on the 95%. Furthermore, the constellation Doppler positional accuracy (3D, 1σ) is superior to 7.8 m on average globally, and better than 15.9 m on the RMS, and better than 42.2 m on the 95%. The constellation Doppler-equivalent PDOP and positional accuracy are optimal at high latitudes, suboptimal at midlatitudes, and relatively poor at low latitudes.

To systematically evaluate the accuracy of GPS LNAV and CNAV broadcast ephemerides and verify their performance in real-time applications, this study conducted a year-long accuracy assessment for 2024 based on GNSS broadcast and precise ephemerides, complemented by validation through real-time orbit determination (RTOD) experiments using LEO satellites. Broadcast and precise ephemerides from 2024 were used to perform statistical analyses of ephemeris accuracy across different satellite blocks and navigation message types. In addition, reduced-dynamic RTOD experiments were carried out using onboard GPS observations from the GRACE-FO C satellite to assess the impact of different broadcast ephemerides on orbit accuracy. The results show that the accuracy of broadcast ephemerides varies significantly among satellite blocks, with BLOCK IIIA performing the best and BLOCK IIF performing the worst. After the clock source switch on G08 and G10 in 2024, the constellation-averaged SISRE for LNAV and CNAV broadcast ephemerides reached 25.5 cm and 23.8 cm, respectively, representing a substantial improvement compared to the LNAV SISRE of 37.0 cm in 2021. The LEO RTOD experiments further demonstrated that, compared to LNAV, CNAV provides improved accuracy in the along-track, cross-track, radial, and 3D directions, with a maximum reduction in 3D orbit error of 0.8 cm and an average improvement of about 3.2%, thereby confirming the advantage of CNAV in real-time applications. Overall, both the ephemeris accuracy statistics and LEO RTOD results consistently indicate that CNAV broadcast ephemerides outperform LNAV. With the gradual retirement of BLOCK IIR satellites and the continued deployment of BLOCK IIIA satellites, navigation, positioning, and timing services based on CNAV broadcast ephemerides are expected to achieve even higher accuracy, further enhancing their value for real-time positioning and scientific applications.

To address the issue of the baffle of antenna cover baffle obstructing signal transmission and receiving of vehicle ground station under low-elevation condition. Firstly, the solution of the foldable mechanism was identified and the bidirectional spring coaxial parallel system which defined the key technology was studied. Secondly, start with the movement process of the folding mechanism, the mathematical model of total system was established, the influencing factors of spring damping deformation and their analytical values were obtained. Then, the dynamic analysis of the spring coaxial parallel system was carried out, and the dynamic parameters of the system were obtained. The obtained three groups parameters were combined with the virtual prototype for dynamic simulation, and through the analysis of the torsional moment curves under the separate action of the inter and outer springs and their joint action. The results showed that the proposed bidirectional spring coaxial parallel system acted alone compared with the inter and outer springs. The value of dynamic peak not only attenuated significantly(approximately 75 %~90 %)but also the dynamic peak frequency was significantly reduced, which verified that the structure with inter and outer springs connected in coaxial parallel had more stable dynamic characteristics, and which provided the favorable reference for other similar engineering projects.

To meet the demand for highly efficient folding and high gain antenna used in CubeSats, a deployable reflector antenna that can be tightly coiled was presented using flexible composite materials. The antenna was mainly composed of flexible reflector, cylindrical shell boom, sub-reflector and feed. The cylindrical shell booms play the role to deploy and maintaining the flexible reflector in the needed shape. One end of each cylindrical shell boom was connected to the periphery of the reflector, while the other one was fixed to the center of the antenna. A simple method was developed to predict the coiling load using elastic Euler beam theory. A 0.5 m prototype antenna was constructed and tested for coiling deployment and RF performance. The diameter and height of the prototype in coiled state were nearly 140 mm and 180 mm separately, and the predicted constraint load was 24% higher than the test value.

With the rapid development of satellite communication technology, the volume of data transmitted between satellites and ground stations continues to increase, driving higher demands for signal transmission rates and quality. In traditional modulators, due to limitations such as DAC(Digital-to-Analog Converter)sampling rates and shaping filter technologies, low-order modulation is often adopted, resulting in lower signal transmission rates that cannot meet the requirements of future high-speed transmission. Therefore, a 16-channel parallel shaping filter technology with a continuously variable transmission rate is proposed in this paper. This technology employs 64APSK(Amplitude Phase Shift Keying)modulation to achieve high-speed data transmission. Experimental results show that, based on the existing hardware platform, data transmission at a rate of 7.2 Gbps is achieved with a 4.8 GHz DAC sampling rate, and the EVM (Error Vector Magnitude)is as low as 2.029 9%. Compared to the original high-speed modulator architecture, this technology offers advantages such as high transmission rates, good signal quality, and low resource consumption, making it a valuable reference for the design of future systems for data transmission exceeding 10 Gbps.

With the increasing application demands for radar jammers, traditional array designs and direction-finding algorithms exhibit critical limitations: excessive information redundancy in uniform arrays, restricted estimation accuracy due to limited physical aperture, and significant performance degradation when processing coherent signals. To address these issues, this paper proposes a coherent signal Direction of Arrival (DOA) estimation method based on spatial smoothing preprocessing and the MUSIC algorithm, integrated with a two-dimensional composite uniform arrays. The method aims to overcome traditional array design constraints and mitigate DOA performance deterioration caused by coherent signals. Specifically, the spatial smoothing technique leverages the translational invariance property of uniform arrays to effectively solve the rank deficiency in covariance matrices induced by coherent signals. The two-dimensional composite uniform array adopts a sparse array configuration to expand the effective aperture under the same number of array elements, thereby enhancing DOA resolution. Experimental validation combines two-dimensional spatial smoothing with the MUSIC algorithm for DOA estimation of coherent signals on the composite array. Results demonstrate that the spatial smoothing method successfully suppresses coherent signal interference, while the composite array achieves superior DOA estimation performance compared to traditional uniform arrays with identical element counts, validating the effectiveness of the proposed methodology.

In the guidance and control of the first-stage recovery of rocket, the carrier pose measurement is very important. Inertial navigation, satellite navigation, LiDAR are usually used to measure the pose in engineering, while there is no precedent for visual measurement. Visual measurement has the advantages of no cumulative error and high update rate, and has the potential to be applied to rocket recovery. In this paper, the dynamics model of rocket recovery is analyzed and trajectory simulation is carried out, and the Falcon9 rocket is taken as the simulation object and the optimal trajectory is obtained by a convex optimization method. Four cameras were installed in the first stage of the rocket, and the visual pose measurement was realized through the multi-vision sensor measurement model, image target recognition and feature picking. The calculation results show that the visual measurement technology proposed in this paper has a high position accuracy and has the prospect of engineering application.