随着对微机电系统-惯性测量单元(micro-electro-mechanical system-inertial measurement unit, MEMS-IMU)在室内定位、动态追踪等应用领域中的需求日益迫切, 使得具有高精度、低成本和实时性的MEMS-IMU模块设计成为研究热点. 针对MEMS-IMU的核心技术——姿态估算进行研究, 设计了一种基于四元数的9轴MEMS-IMU实时姿态估算算法. 该算法运用分解四元数算法处理加速度和磁感应强度数据, 计算出静态四元数; 通过角速度与四元数的微分关系估算动态四元数; 运用卡尔曼滤波融合动、静态四元数, 进而实现实时姿态估算. 针对分解四元数算法中存在的奇异值问题, 提出了转轴补偿方法对其修正, 以实现全姿态估算; 考虑动态情况下的非线性加速度分量对姿态估算精度的影响, 设计了R自适应卡尔曼滤波器, 以进一步提高姿态估算算法的精度. 验证结果表明, R自适应卡尔曼滤波器能够有效抑制加速度噪声, 提高姿态估算精度; 同时, 转轴补偿-分解四元数算法能够准确估算奇异值点的姿态信息, 并且计算时间仅为原“借角”补偿方法的50%左右, 有效提高了整体算法的实时性.
To meet urgent application demands in indoor location and motion tracking, studies on low-cost high-resolution and real-time micro-electro-mechanical system-inertial measurement unit (MEMS-IMU) have attracted much attention. This paper presents a quaternion-based data fusion algorithm for real-time attitude estimation, including factored quaternion algorithm (FQA) for static attitude estimation, and Kalman filtering fordata fusion. A singularity avoidance method, axis-exchanged compensation, is proposed to modify the FQA, allowing the algorithm to track at all attitudes. An R-adapted module is designed to adjust the Kalman gain, which effectively restrains noise due to dynamic nonlinear acceleration, and improves attitude estimation accuracy. Experimental results show that the R-adapted Kalman filter can accurately estimate attitudes in real-time. Additionally, FQA with an axis-exchanged method has good performance in estimating attitudes of singularity points, and the computational efficiency is higher than a previous method by 50%.
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