RESUMO
As various unmanned autonomous driving technologies such as autonomous vehicles and autonomous driving drones are being developed, research on FMCW radar, a sensor related to these technologies, is actively being conducted. The range resolution, which is a parameter for accurately detecting an object in the FMCW radar system, depends on the modulation bandwidth. Expensive radars have a large modulation bandwidth, use the band above 77 GHz, and are mainly used as in-vehicle radar sensors. However, these high-performance radars have the disadvantage of being expensive and burdensome for use in areas that require precise sensors, such as indoor environment motion detection and autonomous drones. In this paper, the range resolution is improved beyond the limited modulation bandwidth by extending the beat frequency signal in the time domain through the proposed Adaptive Mirror Padding and Phase Correction Padding. The proposed algorithm has similar performance in the existing Zero Padding, Mirror Padding, and Range RMSE, but improved results were confirmed through the ρs indicating the size of the side lobe compared to the main lobe and the accurate detection rate of the OS CFAR. In the case of ρs, it was confirmed that with single targets, Adaptive Mirror Padding was improved by about 3 times and Phase Correct Padding was improved by about 6 times compared to the existing algorithm. The results of the OS CFAR were divided into single targets and multiple targets to confirm the performance. In single targets, Adaptive Mirror Padding improved by about 10% and Phase Correct Padding by about 20% compared to the existing algorithm. In multiple targets, Phase Correct Padding improved by about 20% compared to the existing algorithm. The proposed algorithm was verified through the MATLAB Tool and the actual FMCW radar. As the results were similar in the two experimental environments, it was verified that the algorithm works in real radar as well.
RESUMO
Virtual training systems are in an increasing demand because of real-world training, which requires a high cost or accompanying risk, and can be conducted safely through virtual environments. For virtual training to be effective for users, it is important to provide realistic training situations; however, virtual reality (VR) content using VR controllers for experiential learning differ significantly from real content in terms of tangible interactions. In this paper, we propose a method for enhancing the presence and immersion during virtual training by applying various sensors to tangible virtual training as a way to track the movement of real tools used during training and virtualizing the entire body of the actual user for transfer to a virtual environment. The proposed training system connects virtual and real-world spaces through an actual object (e.g., an automobile) to provide the feeling of actual touch during virtual training. Furthermore, the system measures the posture of the tools (steam gun and mop) and the degree of touch and applies them during training (e.g., a steam car wash.) User-testing is conducted to validate the increase in the effectiveness of virtual job training.