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.

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.

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.

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.

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 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.

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 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.

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.

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.