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.

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.

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.

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.

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.

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.

To address the prevalent issues of poor hardware platform versatility and high redundancy development in China's arrow-borne telemetry transmitters, this paper proposes a novel universal design methodology based on operational requirements analysis of existing equipment. By constructing a dynamic reconfigurable intelligent software architecture, diverse mission requirements across different models can be achieved on a universal hardware platform. When mission parameters change, hardware circuit redesign is eliminated; instead, functional updates and parameter adjustments are accomplished via external serial communication with a ground station. Compared to conventional dynamic reconfiguration techniques that only support static parameter modification, this architecture enables real-time online updates and software reconfiguration of telemetry transmitters. Leveraging a crewed spaceflight mission as an engineering case, a multi-code rate arrow-borne telemetry transmitter is developed, with key technologies including universal hardware platform design, real-time health monitoring, and dynamic reconfigurable software architecture thoroughly investigated. Experimental results demonstrate that the proposed solution achieves high integration, autonomous control of domestically produced chips, and real-time health monitoring accuracy. Successful maiden flight validation confirms its engineering feasibility, providing an innovative technical pathway for next-generation arrow-borne telemetry systems.

The objective of coverage path planning is to ensure that Unmanned Aerial Vehicles (UAVs) achieve complete coverage of the target area. Previous studies assigned UAVs the task of covering each sub-area separately. However, this study proposes a new methodology in which two UAVs collaborate across the entire search area, achieving coverage tasks more flexibly while enhancing efficiency. This paper aims to address the high cost of traditional UAV coverage path planning by proposing a dual-UAVcoverage path planning algorithm based on Q-Learning. To reduce the time taken for the process, a grid-based rotating area partitioning algorithm is used to minimize the search area. The path planning is transformed into a multi-objective function optimisation problem, and the Double-Q-Learning algorithm balances global search and local exploitation, iteratively optimising the path with a total cost function that considers distance and turning costs. The simulation results demonstrate that the proposed algorithm can achieve complete coverage of different target areas with a lower total cost.

With the continuous increase of global greenhouse gas concentrations and the escalating issue of climate warming, lidar based on the differential absorption coherent detection principle has become crucial for vertical profiles of greenhouse gas concentrations. To address the technical challenges in current ground-based CO₂ detection lidar systems of simultaneously achieving high integration, high precision, high range resolution, and long-term stability, this study developed a micro-pulse CO₂ profiling lidar operating at 1.57 μm with an all-fiber-integrated architecture. The system combines an injection-locked fiber laser and an off-axis reflective telescope in a coaxial transceiver design, realizing a compact system architecture(0.93 m × 0.34 m × 0.34 m) and sub-picometer wavelength stability (<0.6 pm under 10°C~40°C thermal variations). Horizontal detection experiments as well as continuous observation experiments were conducted. Experiments for horizontal detection revealed a significant linear relationship (R²=0.998) between the logarithmic ratio of dual-wavelength echo power and the detection range, confirming system precision; and the stability of the lidar system was verified by a continuous observation experiment.

This paper designs a miniaturized ultra-wideband variable frequency converter for the Ka -band and focuses on breaking through key technologies for advanced packaging for the Ka-band. In this paper, a ball grid array(BGA) three-dimensional packaging technique is used to achieve multi-chip integration, and a multi-channel wavelength converter compensation circuit for Ka-band is designed, and the vertical transmission of Ka frequency radio signals by BGA within the 32 to 38 GHz band is successfully achieved, further improving the integration of the packaging. A high performance 3D substrate-integrated transmission structure is designed to meet the need of high isolation for interconnection between 3D package modules. For the long-distance coaxial transmission structure, a hierarchical compensation design is adopted to ensure the transmission performance, the plane size of the interconnection structure is reduced by 70% and the inter-line isolation is increased by more than 30 dB. The test results show that the technical specifications of the frequency conversion module are basically consistent with the design results, which provides important guidance and reference for the application of advanced packaging technology in ultra-wideband millimeter wave band.