A guidance law with finite time convergence is designed using the sliding mode control method and finite time convergence control theory, accounting for the missile autopilot as second-order dynamics. The high-order derivatives of the line of sight (LOS) angle are avoided in the expression of guidance law such that it can be implemented in practical applications. The designed guidance law is effective in compensating the bad influence of the autopilot dynamics on guidance accuracy. In simulations of intercepting a non maneuvering target or a maneuvering target, respectively, the designed guidance law is compared with the adaptive sliding mode guidance (ASMG) law in the presence of missile autopilot lag. Simulation results show that the designed guidance law is able to guide a missile to accurately intercept a nonmaneuvering target or a maneuvering target with finite time, even if it escapes in a great and fast maneuver and the autopilot has a relatively large lag.

With the construction and development of the BeiDou navigation satellite system (BDS), the precise point positioning (PPP) performance of the BDS is worthy of research. In this study, observational data from 17 stations around the world across 20 days are used to comprehensively evaluate the PPP performance of BDS B1c/B2a signals. For greater understanding, the results are also compared with the Global Positioning System (GPS) and BDS PPP performance of different signals and system combinations. The evaluation found root mean square (RMS) values of the static PPP in the north (N), east (E), and upward (U) components, based on the B1c/B2a frequency of BDS-3, to be 6.9 mm, 4.7 mm, and 26.6 mm, respectively. Similar to the static positioning, the RMS values of kinematic PPP in the three directions of N, E, and U are 2.6 cm, 6.0 cm, and 8.5 cm, respectively. Besides this, the static PPP of BDS-3 (B1cB2a) and BDS-2 + BDS-3 (B1IB3I) have obvious system bias. Compared with static PPP, kinematic PPP is more sensitive to the number of satellites, and the coordinate accuracy in three dimensions can be increased by 27% with the combination of GPS (L1L2) and BDS. Compared with BDS-2+BDS-3 (B1IB3I), the convergence time of BDS-3 (B1CB2a) performs better in both static and kinematic modes. The antenna model does not show a significant difference in terms of the effect of the convergence speed, though the number of satellites observed has a certain influence on the convergence time.

The issue of relative positioning in Unmanned Aerial Vehicle (UAV) swarms is critical for effective cooperative management and network construction. However, current low-cost BeiDou System (BDS) exhibit poor accuracy, typically ranging from several meters to tens of meters, which fails to meet the basic requirements for cooperative positioning. To address this challenge, a combined positioning architecture based on BDS, Barometer, and Ultra-Wideband (UWB), alongside an accuracy optimization algorithm using gradient descent, is proposed. Firstly, existing UAV swarm positioning technologies and algorithms are analyzed. By combining data from BDS, barometer, and UWB sensors, the accuracy problem of the combined system was transformed into an optimization problem. Then, models such as data credibility evaluation, information difference calculation, and gradient descent optimization were used to solve the optimization problem. Finally, a precision optimized BDS/Barometer/UWB combined positioning architecture is proposed. Simulations and real-world experiments demonstrate that the fusion positioning architecture achieves higher cooperative positioning accuracy compared to mainstream systems such as Inertial Navigation System (INS) and BDS. The final distance Root Mean Square Error (RMSE) is reduced to 1.313 m, a 69.0% improvement over the 4.231 m observed with standalone BDS.

In low-altitude environments, UAV obstacle detection faces significant challenges due to the small size of obstacles, variable weather conditions, and interference from signal disruptions, all of which severely impact flight safety. To address these issues, this paper proposes a bidirectional-extended-path aggregation network to enhance multi-scale feature fusion, along with the introduction of a balanced attention module to improve detection accuracy in complex scenarios. Additionally, the paper introduces the global sensory mamba module and ripple manhattan self-attention models to further strengthen the network’s learning capabilities, thereby enhancing detection precision in challenging environments. The Bidirectional Global Ripple Low-altitude YOLO(BGR-YOLO) model, which integrates the above structures with YOLOv1ln, is proposed and tested on a UAV flight safety dataset for low-altitude environments. Experimental results demonstrate a significant improvement, with mean average precision (mAP _0.5 ) increasing from 85.3% to 90.5%, and mAP _0.5 rising from 62.8% to 67.9%, without a notable increase in parameters. Furthermore, results on the VisDrone2019 and SODA-A datasets confirm that this method provides an effective solution for detecting low-altitude obstacles in complex environments.

Accounting for the autopilot as second-order dynamics, an observer-based guidance law with terminal impact angle constraint is designed using the dynamic surface control method. Some first-order low-pass filters are introduced into the designing process to avoid the occurrence of high-order derivatives of the line of sight (LOS) angle in the expression of the guidance law such that the guidance law can be implemented in practical applications. The proposed guidance law is effective in compensating for the second-order autopilot lag. In simulation of intercepting targets with sinusoidal acceleration, the guidance law is compared with the biased proportional navigation guidance (BPNG) law in the presence of missile autopilot lag. Simulation results show that the proposed observer-based guidance law with terminal impact angle constraint is able to guide a missile with large autopilot lag to impact a target with a desired angle and achieve a small miss distance, even if the target escapes in a great and fast maneuver.

The vulnerability of Global Navigation Satellite Systems (GNSS) to spoofing limits their widespread use in military security and national economy. Therefore, fast and accurate detection of GNSS spoofing is of great significance. When spoofing cannot be accurately detected in the capture tracking phase, spoofing detection needs to be performed again at the localization solver. In order to detect the spoofing jamming of Global Navigation Satellite System in pseudo-range measurements, a generalized likelihood ratio satellite navigation spoofing detection algorithm based on moving variance is proposed by analyzing the pseudo-ranges cleared by the positioning of global satellite navigation signals. A new data subset is created by calculating the variance of the pseudo-range of different satellites at the same time and moving it forward. The variance is calculated again by this data subset to obtain the moving variance, the generalized likelihood ratio detection model is used to calculate the detection statistics of the pseudo-range movement variance, the detection statistic is then compared to the detection threshold under the condition that the probability of false alarm is 1×10 ^-7 , so as to realize the spoofing jamming detection of global satellite navigation receiver for pseudo-range. Taking the software receiver as the experimental platform, the effectiveness of the proposed algorithm is verified by comparing it with two other algorithms. The result show that when the number of spoofed satellites is less than 9, the algorithm has a good detection effect. When the false alarm rate is 1×10 ^-7 , the average prediction accuracy rate is kept above 98 %.