用UWB和车载传感器在十字路口定位车辆的融合策略
Huaikun Gao1, Xu Li1, Xiang Song2
1School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究提出了一种用于使用超宽带 (UWB) 和车载传感器可靠定位车辆的融合策略. 该方法有效地解决了城市交通中的非视线 (NLOS) 问题,提高了定位准确性.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 无线定位技术无线定位技术
- 传感器融合式传感器
背景情况:
- 高精度的车辆定位对于智能运输系统 (ITS) 至关重要.
- 超宽带宽 (UWB) 提供了高精度定位,但在城市交通中受到非视线 (NLOS) 传播和多路径效应的影响.
- 现有的UWB定位方法需要改进,以确保在具有挑战性的交通环境中可靠性.
研究的目的:
- 通过整合UWB和车载传感器数据,开发一个强大的融合战略,以实现可靠和精确的车辆定位.
- 解决和减轻非视线 (NLOS) 传播和其他干扰对UWB定位在车辆交通场景中的影响.
- 提高车辆定位系统在动态驾驶条件下的准确性和适应性.
主要方法:
- 一种两步的方法,涉及UWB原始测量预处理和全球位置估计.
- 开发ARIMA-GARCH模型,以有效检测和纠正UWB非视线 (NLOS) 错误.
- 实现全球传感器融合的自适应交互多模型 (AIMM) 算法,通过调整模型概率以适应当前的驾驶条件,优于传统的IMM.
主要成果:
- 拟议的ARIMA-GARCH模型有效地检测和纠正车辆交通中的UWB NLOS错误.
- 通过动态调整模型概率,自适应性IMM (AIMM) 算法证明了更好的定位准确性.
- 实验验证证了UWB和车载传感器融合战略的有效性和可行性.
结论:
- 开发的融合战略为智能运输系统 (ITS) 中的车辆定位提供了可靠和精确的解决方案.
- 将UWB与车载传感器集成,加上先进的错误校正和自适应融合技术,显著提高了本地化性能.
- 拟议的方法提供了一种实用和有效的方法,用于在复杂的城市环境中实现车道水平或更高精度的定位.
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