The accuracy of attitude and heading measurement, as well as the system real-time performance are basic indicators used to evaluate an attitude and heading reference system (AHRS). In order to improve the attitude and heading measurement accuracy under dynamic complex environment, the AHRS system should also have numerical stability and calculation robustness. The AHRS system based on MEMS multi-sensor fusion can realize fusion processing of data measured by multiple sensors, so as to calculate and obtain the optimal carrier attitude and heading information, conduct real-time output, and improve the accuracy and reliability of attitude and heading measurement. For the AHRS system consisting of MEMS gyroscope, accelerometer and triaxial magnetometer, attitude and heading detection principle and algorithm based on MEMS multi-sensor fusion were proposed in this study: The information of the system itself was firstly used to discriminate motion state of the carrier within the filtering cycle, and then Kalman filtering was conducted using different measured information according to motion state to correct the attitude error angle caused by gyroscopic drift. On this basis, an attitude fusion algorithm based on extended Kalman filtering technology was designed for time update process of Kalman filtering, output information of accelerometer was taken as observed quantity under certain conditions to realize measurement updating process of Kalman filtering, and then attitude angle was calculated. In an optical fiber attitude and heading system project in practical engineering, a vehicle field test analysis was carried out simultaneously with the system using ordinary attitude algorithm, and the results showed that the extended Kalman filtering algorithm designed according to the simulation results could realize multi-sensor information fusion, improve measurement accuracy and realize accurate attitude positioning, so as to provide simpler and more flexible criteria for carrier motion status. The results have verified the accuracy and reliability of the algorithm, so it is feasible in practical engineering.

中文翻译：

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基于MEMS多传感器融合的扩展卡尔曼滤波器姿态航向参考系统设计与实现

姿态和航向测量的准确性以及系统实时性能是评估姿态和航向参考系统（AHRS）的基本指标。为了提高动态复杂环境下的姿态和航向测量精度，AHRS系统还应具有数值稳定性和计算鲁棒性。基于MEMS多传感器融合的AHRS系统可以实现多传感器测量数据的融合处理，从而计算得到最优的载体姿态和航向信息，进行实时输出，提高姿态和航向的准确性和可靠性。航向测量。对于由MEMS陀螺仪、加速度计和三轴磁力计组成的AHRS系统，本研究提出了基于MEMS多传感器融合的姿态航向检测原理和算法：首先利用系统自身的信息来判别载体在滤波周期内的运动状态，然后利用不同的测量值进行卡尔曼滤波。根据运动状态信息修正陀螺漂移引起的姿态误差角。在此基础上，针对卡尔曼滤波的时间更新过程，设计了一种基于扩展卡尔曼滤波技术的姿态融合算法，以加速度计的输出信息为一定条件下的观测量，实现卡尔曼滤波的测量更新过程，进而得到姿态角。计算出来的。在实际工程中的一个光纤姿态航向系统项目中，采用普通姿态算法与系统同时进行了车辆现场试验分析，结果表明，根据仿真结果设计的扩展卡尔曼滤波算法可以实现多传感器信息融合，提高测量精度，实现准确姿态定位，从而为载体运动状态提供更简单、更灵活的标准。结果验证了算法的准确性和可靠性，在实际工程中是可行的。从而为载体运动状态提供更简单、更灵活的标准。结果验证了算法的准确性和可靠性，在实际工程中是可行的。从而为载体运动状态提供更简单、更灵活的标准。结果验证了算法的准确性和可靠性，在实际工程中是可行的。