Unmanned aerial vehicles (UAVs) have demonstrated exceptional capabilities in various applications such as vehicle tracking, military monitoring and communication assistance. With their advantages in mobile communication and integrated multiple sensors, UAVs have great potential to obtain the integration gain through integrated sensing and communications (ISAC) designs. In this paper we focus on the beamforming algorithm for UAV-to-vehicle communications. To reduce the communication overhead of block transmission caused by the high mobility of the target vehicles and the UAV, we utilize the inherent vision functionality of the UAV platform and propose a vision-assisted beamforming framework. Specifically, to obtain the position of to-be-served vehicles, we employ YOLOv5, a deep learning model, for vehicle detection, followed by a Sage-Husa filter for multiple target vehicle tracking. Based on the predicted positions of the vehicles, we propose a lightweight beamforming algorithm to save communication overhead of beam tracking. To validate the effectivess of this work, we perform experiments on the UAVDT dataset for vehicle detection and tracking. The simulation results show that the proposed algorithm achieves significant performance gains on both the effective achievable rates and the energy efficiency.

In various practical applications, such as autonomous vehicle and unmanned aerial vehicle navigation, Global Navigation Satellite Systems (GNSSs) are commonly used for positioning. However, traditional GNSS positioning methods are often affected by disturbances due to external observational conditions. For instance, in areas with dense buildings, tree cover, or tunnels, GNSS signals may be obstructed, resulting in positioning failures or decreased accuracy. Therefore, improving the accuracy and stability of GNSS positioning in these complex environments is a critical concern. In this paper, we propose a novel multi-sensor fusion framework based on multi-frame residual optimization for GNSS/INS/LiDAR to address the challenges posed by complex satellite environments. Our system employs a novel residual detection and optimization method for continuous-time GNSS within keyframes. Specifically, we use rough pose measurements from LiDAR to extract keyframes for the global system. Within these keyframes, the multi-frame residuals of GNSS and IMU are estimated using the Median Absolute Deviation (MAD) and subsequently employed for the degradation detection and sliding window optimization of the GNSS. Building on this, we employ a two-stage factor graph optimization strategy, significantly improving positioning accuracy, especially in environments with limited GNSS signals. To validate the effectiveness of our approach, we assess the system’s performance on the publicly available UrbanLoco dataset and conduct experiments in real-world environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other related frameworks.

The rapid growth of internet of things (IoT) applications has driven the demand for high-accuracy indoor localization. However, existing mainstream solutions have distinct limitations: while sensors such as vision, inertial, and light detection and ranging (LiDAR) provide high-precision relative motion tracking, their inherent cumulative drift compromises long-term robustness; meanwhile, high-precision technologies like ultrawideband (UWB) rely on expensive, dedicated infrastructure deployment. The emergence of 5G networks offers a unique solution by utilizing their existing communication base stations as positioning anchors. Leveraging its wideband signals, 5G has the potential to provide the absolute position References needed to correct relative errors without significant additional hardware costs, thereby overcoming the limitations of the aforementioned techniques. However, 5G-based positioning technologies face challenges, such as multipath interference, clock synchronization errors, and degraded performance during terminal mobility. To address these limitations, we propose a novel, tightly coupled 5G positioning reference signal (PRS) and inertial measurement unit (IMU) fusion framework. The core of our approach is a two-stage architecture. First, a front-end signal error estimator (EE) models the joint estimation of clock offset and integer ambiguity as a mixed-integer nonlinear programming (MINLP) problem. Crucially, it leverages IMU-predicted kinematics as a strong physical constraint to effectively identify and suppress measurements contaminated by multipath. Subsequently, a back-end, tightly coupled error-state Kalman filter (ESKF) fuses the time-differenced carrier phase (TDCP) and time difference of arrival (TDOA) measurements from the front-end with inertial navigation data. Simulation results conducted under 3GPP standard scenarios demonstrate that the proposed framework achieves a positioning accuracy improvement of approximately 40% in centimeter-level accuracy, in line-of-sight (LOS) environments with tactical IMU equipment. This provides a promising solution for 5G-based indoor localization systems. In real-world indoor field tests characterized by multipath effects, our proposed EE-ESKF algorithm achieved a 3-D root mean square error (RMSE) of 0.55 m, which is 33.6% higher than the standard ESKF algorithm.

Future-oriented integration of sensing and communication and perception-assisted communication technology are arousing the attention of researchers. In this paper, we propose a new vision-based beamforming communication system framework. A vision aided beamforming scheme based on unmanned airship to ground multi-user is proposed. To help beamforming, we calculate the position and angle of the ground user through the captured image. In particular, the model based on CBAM and YOLO is used for target detection. Then, a zero-forcing beamforming algorithm con-sidering inter-user interference is proposed, which is able to eliminate the interference of beamforming signals from other users on the current user and improve the multi-user performance.