Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

In recent advancements in sensor and artificial intelligence technologies, the reliability of gesture recognition has significantly improved, prompting various industrial fields to adopt this technology. However, most gesture recognition systems rely on optical methods because of their high accuracy, despite require complex and computationally intensive processes. Moreover, these systems are associated with high construction costs and are susceptible to environmental factors. This paper introduces a novel gesture recognition system, which effectively tracks and estimates gestures using cost-effective ultra-wideband (UWB) sensors and inertial measurement units (IMU). The system acquires position data of gesture through UWB sensors and includes essential data processing steps such as the detection and removal of abnormal data via IMU, data smoothing with a Kalman filter, and data normalization and scaling. Notably, normalization and scaling are achieved by converting the position data into grayscale images, ensuring the consistency of data features and enhancing gesture recognition accuracy across diverse users. The proposed system employs a convolutional neural network (CNN) model to estimate gestures from these images. Comparative analyses demonstrate that the proposed system exhibits superior gesture classification performance compared to systems utilizing a long-short term memory (LSTM) model and those employing the same CNN model without the aforementioned data processing steps. Therefore, this system is not only cost-effective but also efficiently tracks and estimates gestures, offering significant improvements over existing methods.