Beidou Satellite Navigation System (BDS) navigation receiver has the functions of Positioning, Navigation, and Timing (PNT) and message communication, and has been widely used in various industries. Under the background of the smooth transition from BDS-2 regional system to BDS-3 global system, its influence on the service performance of the navigation receiver and countermeasures are studied. The differences between BDS-2 and BDS-3 in signal type, signal system, constellation scale and service performance are compared, and the specific manifestations and state change trends of BDS smooth transition are expounded. The impact of the smooth transition of BDS on the service performance of navigation receiver is analyzed emphatically, including navigation and positioning, message communication and anti-suppression-jamming ability. The simulation results show that the RDSS message communication service can still be used normally during the smooth transition period of BDS-2 receiver ( PRN01～37). With the progressive retirement of BDS-2 satellite, the number of satellites available in space will gradually decrease from 33 to 18, the average number of satellites visible worldwide will decrease from 11.62 to 6.31, the average Geometric Dilution Precision (GDOP) value will increase from 2.00 to 3.15, and the continuous availability of services will decrease from 93% to 46.46%, which will affect the positioning accuracy and service range. When the power of BDS-3 satellite is enhanced, the navigation receiver can obtain 7～15 dB improvement in anti-suppression-jamming ability. According to the different application scenarios of the navigation receiver, the corresponding countermeasures are given to weaken or eliminate the impact, so that the navigation receiver can continuously provide reliable services for users during the smooth transition of the BDS. The research results can provide reference for the design, development and application of BDS navigation receivers.

Vision-Language-Action (VLA) models are a core technology for achieving general embodied artificial intelligence, aiming to integrate visual perception, language understanding, and action decision-making within a unified end-to-end framework. The current research status and development trajectory of VLA models are comprehensively and systematically reviewed. The theoretical origins of VLA models are traced, and the paradigm shift from modular designs to unified architectures is clarified. Along the evolutionary path of VLA, representative works such as SpatialVLA, TLA, and GR00T N1 are presented with a focus on multimodal fusion and cognitive hierarchies. A detailed taxonomy of VLA models is constructed from two key dimensions-macro architecture and system hierarchy. Key technologies and design principles are deeply analyzed, ranging from pioneering works such as RT-1, to models introducing large-scale knowledge transfer such as RT-2, OpenVLA, and ECOT, and further to cutting-edge dual-system architectures such as Helix, OpenHelix, DexVLA, and DexGraspVLA. Mainstream simulation environments, core datasets, and benchmarks supporting VLA research are systematically integrated and reviewed. The application status and prospects of VLA models in robotic manipulation, autonomous navigation, and industrial automation are explored. Core challenges in current VLA research are analyzed, including generalization and data efficiency, long-horizon task planning, and real-time responsiveness. Future research directions are discussed, including integration with world models and enhancement of data efficiency.

In traffic surveillance systems, radar-camera devices are used to collaboratively perceive and monitor the roadside environment. Due to the principles of perspective imaging, the greater the distance to a target, the smaller its corresponding pixel area in the image. Furthermore, the bounding boxes generated by visual detection exhibit significant jitter. If calibration errors or visual occlusion exist, or if the detection boxes shake, a significant error will be introduced when the target' s position is mapped from the image coordinate system to the radar coordinate system, affecting tracking accuracy. This is especially true for collaborative target sensing and tracking with multiple sensors, which further increases the difficulty. To address these challenges, a multi-sensor, multi-target collaborative perception and tracking method is proposed, leveraging a two-stage matching strategy and an adaptive Kalman filter. This method improves association precision by adding a secondary matching strategy of Perspective View (PV) plane after the Bird's Eye View (BEV) plane is associated with the data of frame before and after. This effectively solves the problem of low tracking accuracy for distant targets caused by significant mapping errors. Based on the relationship model between image points and range-position jitter, an adaptive multi-sensor multi-target tracking method is proposed. By using the relationship model to update the parameters of the Kalman filter, and adaptively selecting the appropriate observation matrix and measurement covariance matrix according to the target sensor data source, the position and velocity parameters of the target are estimated. This effectively improves the real-time prediction accuracy of the target' s spatial position and velocity, and further enhances the accuracy of target association in the BEV plane. Experimental results show that the proposed method improves the Multiple Object Tracking Accuracy ( MOTA) index by 16.3% compared to the method without the two-stage matching strategy and only using the ordinary Kalman filter, significantly improving the accuracy of target perception and tracking in traffic scenes using millimeter-wave radar and vision integrated systems.

To solve the problem of stable information transmission for unmanned systems in complex electromagnetic environments, the information elastic adaptation method for unmanned systems based on communication quality assessment is proposed. Signal strength, bit error rate, and signal-to-noise ratio at the communication link layer are selected as characteristic parameters for communication quality assessment. Long Short-Term Memory ( LSTM) network is served as the signal prediction model to estimate future signal parameters, while a Support Vector Regression (SVR) model is employed to evaluate real-time communication quality. Based on the communication quality level evaluation model, the communication quality evaluation results are obtained; corresponding level of content is transmitted according to the communication quality level. Simulations using real-world data and field tests demonstrate that the proposed method ensures reliable information transmission for unmanned systems in highly dynamic and contested electromagnetic environments.

To solve the problems of tracking and positioning of airborne early warning aircraft and other long-range targets，a high-precision passive positioning strategy based on the collaborative networking of UAVs and passive radar is proposed. By analyzing the impact of the layout of multi-station passive radar on positioning accuracy，a layout strategy that can improve positioning accuracy and reduce layout costs is designed. Time Difference of Arrival (TDOA) positioning method is applied in the study，and on this basis，the Geometric Dilution of Precision ( GDOP) for multiple stations is analyzed to evaluate the impact of the layout form on positioning accuracy. A collaborative strategy is proposed that，when the initial direction of the target is unknown，first uses a star-shaped layout for initial positioning and then switches to an inverted triangular layout for high-precision secondary positioning. The optimal secondary stations are selected using the “virtual structure method”and the flight trajectory of the UAV is optimized using an improved Particle Swarm Optimization ( PSO) algorithm to achieve high-precision layout. Simulation results show that this strategy can significantly improve positioning accuracy. Compared with traditional passive radar systems，positioning error is significantly reduced，and system response speed is faster. The research results have certain application value in practice.

With the development of technologies such as artificial intelligence, multi-agents ( e. g. , unmanned aerial vehicle swarms) have been increasingly applied in practical combat operations. The Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, designed to solve the coordination problems of multi-agents in cooperative environments, has become one of the mainstream applied algorithms in the multi-agent field owing to its unique Actor-Critic framework. To address the problems in multi-agent collaborative tasks during command and decision-making—including ambiguous role division and slow convergence of the algorithm's policy caused by information overload—an improved MADDPG algorithm incorporating a Dynamic Role Attention(DRA) mechanism, namely DRA-MADDPG, is proposed. This algorithm embeds a DRA module into the Actor-Critic framework, and achieves accurate optimization of division of labor and collaboration by dynamically adjusting the attention weights of each agent towards peers with different roles. Specifically, the role set ( reconnaissance, assault, command) and phase division ( exploration→execution→encirclement) for command tasks are defined, and on this basis, a role coordination matrix and phase adjustment coefficients are constructed. A DRA module is designed in the Critic network to calculate weights and filter key information by leveraging role relevance and task phases. Additionally, the Actor network is improved to generate targeted actions by integrating role responsibilities. Simulation experiments show that compared with MADDPG, the Area Under the Curve (AUC) of the cumulative training reward of DRA-MADDPG increases by 2.4%, and the task completion time decreases by 19.3%. Furthermore, comparative analysis of training reward curves reveals that DRA-MADDPG exhibits better learning efficiency in short-term training. It is demonstrated that this method is suitable for complex command and decision-making scenarios and provides a relatively efficient solution for multi-agent coordination.

The rising incidence of skin lesions has made early screening for skin cancer increasingly critical. However, existing methods for skin lesion image segmentation often suffer from limitations in channel-wise information modeling, structural adaptability, and feature fusion, which can lead to inaccurate boundary delineation and insufficient utilization of crucial contextual information. To address these issues, a skin lesions image segmentation method based on attention mechanism and wavelet transform, termed AW-SkinNet, is proposed. The proposed approach employs a dual-branch collaborative attention module to extract spatial and channel-dependent features, integrates wavelet transform to enhance frequency-domain representations, and incorporates lightweight attention-guided sub-pixel upsampling to improve detail restoration and contextual understanding. Experimental results on the ISIC-2017 and ISIC-2018 skin lesion segmentation datasets demonstrate that the proposed method achieves higher segmentation accuracy compared with existing approaches for skin lesion image segmentation.

To meet the requirements of half/full duplex communication systems, a dual-frequency omnidirectional dual-circularly-polarized Multiple Input Multiple Output (MIMO) antenna based on magnetic-electric dipole is proposed. It consists of four identical antenna elements symmetrically arranged to form a 2×2 MIMO array, and each antenna element is composed of circular planar waveguide, four folded electric dipoles, and eight parasitic electric dipoles. The slits on the circular planar waveguide and short-circuit cylinders are employed to form four magnetic dipoles on the opening side of the circular planar waveguide, and their radiated E-field is orthogonal to the E-field of the electric dipole. When a 90° phase difference between the magnetic dipole and the electric dipole is realized due to their spatial distance, a left-handed or right-handed circularly polarized wave with 360° coverage can be produced. The results demonstrate that each antenna element can radiate right-handed circularly polarized wave in low bands (2.42～2.47 GHz) and left-handed circularly polarize wave in high band (5.76～5.85 GHz), its Axial Ratio (AR) is less than 3 dB, and the gain fluctuation is less than 2.1 dB and 7.3 dB, respectively. Moreover, the isolation is lower than -30 dB in low band and -50 dB in high band respectively owing to symmetrical distributions between adjacent elements.

A wideband radial power combiner based on Ridge Gap Waveguide ( RGW) is designed for high-power combining applications in the wide millimeter-wave frequency band. The combiner consists of two metallic plates, with the center of the lower plate fed by a coaxial line. The energy is reflected by multiple metallic conical structures positioned above the plate, and directed into the radial transmission lines, achieving equal power division. The radial transmission lines employ ridge gap waveguides, which is built up by placing pin-type Electromagnetic Band-Gap (EBG) structures on the metal bottom plates adjacent to the ridge, effectively reducing coupling among adjacent channels without the need for welding the upper and lower plates. Simulation results show that the combiner operates within the frequency range from 14.7 to 37.5 GHz, with a reflection coefficient of less than -15 dB and a transmission coefficient of approximately -6.1 dB. The measurement results show good agreement with the simulation results.

To solve the problem that Automatic Modulation Recognition (AMR) is limited by small-sample data and insufficient fusion of time-frequency multimodal information in practical applications, which in turn leads to low recognition accuracy, the limitations of existing technologies in the AMR field are analyzed and a cross-modal self-supervised learning framework integrating a diffusion model and a contrastive learning mechanism is proposed. By introducing the diffusion model, the framework leverages its generative capability to achieve high-quality data synthesis and augmentation of communication signals, effectively alleviating the constraints of small-sample data on model training. Meanwhile, combined with the cross-modal contrastive learning mechanism, it constructs an inter-modal association learning module to fully explore and utilize the inherent correlations and complementary information between different time-frequency modal representations, thus solving the problem of insufficient multimodal information fusion. Finally, based on the above design, a Diffusion-Contrastive Hybrid Network (DCHN) model is established. Experimental results show that the recognition accuracy of this model on the RML2016.10a dataset is significantly higher than that of other network models, indicating that it possesses excellent recognition capability.