基于MEMS-IMU和外部加速补偿的低动态车辆的稳健态度估计
Jiaxuan Chen1,2, Bingbo Cui1,2, Xinhua Wei1,2
1Key Laboratory of Modern Agricultural Equipment and Technology, Jiangsu University, Ministry of Education, Zhenjiang 212013, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
这项研究引入了强大的卡尔曼波器 (RKF),以提高使用微电机系统惯性测量单元 (MEMS-IMU) 的车辆的姿态估计准确度. RKF可自适应地补偿外部加速,在具有挑战性的条件下显著提高性能.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 传感器融合和导航
- 机械工程 机械工程
背景情况:
- 微电机系统惯性测量单元 (MEMS-IMU) 对于定位定位至关重要.
- 非重力加速度显著影响MEMS-IMU态度估计的准确性.
- 现有的方法在振动和不平坦的地形下与低动态车辆作斗争.
研究的目的:
- 开发一个强大的卡尔曼波器 (RKF) 来改进MEMS-IMU态度估计.
- 为了提高低动态车辆定位的准确性和可靠性.
- 在现实世界条件下补偿外部加速干扰.
主要方法:
- 使用简化的方向等号矩阵构建了用于MEMS-IMU态度估计的状态模型.
- 开发了一个RKF,可以自适应地调整噪声共变量,以补偿外部加速.
- 估计未建模的外部加速变异在线使用过创新.
- 通过对三轴转盘,自动车辆和拖拉机耕地测试的实验验证实了RKF.
主要成果:
- 在转盘上,RKF实现了0.051°的根平均平方误差 (RMSE),超过了传统的卡尔曼波器 (KF) 和MTi-300.
- 对自动车辆的动态态度估计显示,RKF与KF相比,RKF的俯仰角更为平滑,RMSE从0.875°降低到0.460°.
- 拖拉机耕地测试表明,RKF (0.259°) 与KF (0.493°) 相比,RKF (0.259°) 的地RMSE有47.5%的改善.
结论:
- 开发的RKF有效地抑制了基于MEMS-IMU的态度估计中的外部加速干扰.
- 在充满挑战的车辆动态和环境中,RKF提供了卓越的性能和准确度,用于定位.
- 适应性噪声协变补偿是RKF增强稳健性和可靠性的关键.
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