一个基于机器学习的新型ANFIS校准了RISS/GNSS集成,以改善城市环境中的导航
Ahmed E Mahdi1, Ahmed Azouz1, Aboelmagd Noureldin2
1Electrical Engineering Branch, Military Technical College (MTC), Cairo 11766, Egypt.
Sensors (Basel, Switzerland)
|March 28, 2024
概括
自动驾驶汽车需要可靠的导航. 将校准的减少惯性传感器系统 (RISS) 与使用自适应神经模糊推理系统 (ANFIS) 的全球导航卫星系统 (GNSS) 集成,可显著提高城市地区的定位精度.
科学领域:
- 机器人技术和自主系统
- 导航和定位 导航和定位
- 机器学习应用 机器学习应用
背景情况:
- 全球导航卫星系统 (GNSS) 对于自动驾驶汽车 (AV) 导航至关重要,但由于信号阻塞和多路径干扰,在城市环境中存在可靠性问题.
- 对于AV的传统传感器集成方法,在降低的GNSS条件下经常难以保持准确性.
- 惯性导航系统 (INS),特别是减少惯性传感器系统 (RISS),提供了一个替代方案,但需要精确的校准来减轻漂移.
研究的目的:
- 提出和验证一种新的传感器融合方法,以提高自动驾驶汽车在具有挑战性的城市环境中的导航精度和可靠性.
- 引入适应性神经模糊推理系统 (ANFIS) 作为基于机器学习的RISS校准技术.
- 评估ANFIS校准的RISS/GNSS集成系统与传统RISS/GNSS和基于雷达的集成系统的性能.
主要方法:
- 开发一个导航系统,将全球导航卫星系统 (GNSS) 与校准的减少惯性传感器系统 (RISS) 集成在一起.
- 基于机器学习的自适应神经模糊推理系统 (ANFIS) 的实施,用于新的RISS校准.
- 通过现实道路轨迹测试和模拟不同时间 (50-150秒) 的GNSS中断进行验证.
主要成果:
- 与传统的RISS/GNSS相比,基于ANFIS的RISS/GNSS集成显示,2D位置中位平方根误差 (RMSE) 显著减少43.8%.
- 与频率调制连续波 (FMCW) 雷达 (Rad) /RISS/GNSS综合系统相比,观察到2D位置RMSE的28%改善.
- 该系统实现了对二维位置最大误差的大幅降低:47.5%与RISS/GNSS相比,23.4%与Rad/RISS/GNSS相比.
结论:
- 拟议的基于ANFIS的RISS/GNSS集成提供了卓越的定位精度和可靠性,这对于在城市环境中安全自动驾驶车辆操作至关重要.
- 该系统具有长期稳定性,适用于要求连续,精确定位的应用.
- 该ANFIS校准方法可扩展到其他低成本的惯性测量单元 (IMU),为各种导航应用提供了多功能和有吸引力的解决方案.
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