一个自我学习的平均优化过器,以提高蓝牙5.1 AoA室内定位准确度,用于船舶环境
Qianfeng Lin1, Jooyoung Son2, Hyeongseol Shin3
1Department of Computer Engineering, Korea Maritime and Ocean University, 727 Taejong-ro, Yeongdo-Gu, Busan 49112, South Korea.
Journal of King Saud University. Computer and information sciences
|July 31, 2023
概括
本研究介绍了一种自学平均优化波器 (SLMOF),以提高蓝牙5.1到达角度 (AoA) 船舶的室内定位精度. SLMOF过器显著提高了定位准确度,这对于在海上环境中追踪COVID-19接触者至关重要.
科学领域:
- 海洋技术的海洋技术
- 无线通信是一种无线通信.
- 信号处理 信号处理
背景情况:
- 在封闭的船舶环境中,COVID-19的传播是一个重大风险.
- 准确的室内定位对于有效的接触者追踪和海上疾病控制至关重要.
- 现有的蓝牙5.1到达角 (AoA) 定位面临挑战,原因是船舶设置中的多路径效应和噪声.
研究的目的:
- 开发一种先进的过方法,以提高蓝牙5.1 AoA在船舶环境中的室内定位精度.
- 为了解决由信号噪声引起的高度和度角的不稳定性.
- 为了提高室内定位的可靠性,用于海上健康和安全应用.
主要方法:
- 实现一个自学平均优化波器 (SLMOF) 算法.
- 使用蓝牙5.1到达角度 (AoA) 技术与统一矩形阵列 (URA) 天线.
- 对卡尔曼波器 (KF) 进行测试和比较,以评估性能.
主要成果:
- 拟议的SLMOF实现了0.44m的根平均平方误差 (RMSE).
- 与卡尔曼波器 (KF) 相比,SLMOF在定位准确度上有72%的改进.
- 过器有效地优化了升高和光向角,减少了噪声的影响.
结论:
- 自学平均优化过器 (SLMOF) 在具有挑战性的船舶环境中显著提高了蓝牙5.1 AoA室内定位准确性.
- 这项技术为精确的接触追踪和降低船舶上传染病风险提供了可行的解决方案.
- SLMOF方法在数据集中找到最佳平均值的能力为改进无线定位系统提供了强大的方法.
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