Mohamed S. Elsabbagh, A. H. Hassaballa, A. Kamel, Y. Elhalwagy
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引用次数: 1
摘要
设计了一种自适应无气味卡尔曼滤波(AUKF),利用低成本惯性传感器估计刚体的横滚角和俯仰角。主要的挑战是在高动态环境下的倾斜方向估计,其中线性加速度会影响方向精度。该滤波器基于四元数技术和用于补偿运动过程中加速度计修正影响的加性函数。该算法在STM32F407 ARM Cortex-M4系列微控制器和基于微机电系统(MEMS)技术的融合三轴加速度计和三个单轴陀螺仪三联上实现。实验和现场测试结果分析表明,与传统KF和其他昂贵的商用系统相比,该系统具有出色的实时导航性能。
Precise Orientation Estimation Based on Nonlinear Modeling and Quaternion Transformations for Low Cost Navigation Systems
An adaptive unscented Kalman filter (AUKF) is designed to estimate the roll and pitch angles of rigid body using low-cost inertial sensors. The main challenge is concerned about the tilt orientation estimation in high dynamic environments, where the linear acceleration affects the orientation accuracy. The proposed filter is based on the quaternion technique and an additive function which is used to compensate the influence of accelerometer corrections during motions. The algorithm is implemented on a STM32F407 ARM Cortex-M4 series microcontrollers and fused three-axis accelerometer and, three single-axis gyroscopes triads based on micro-electromechanical system (MEMS) technology. The experimental and field tests results analysis showed an outstanding real-time navigation performance when compared with the traditional KF and other commercial expensive systems.