CHEN Guangwu1,2, FAN Ziyan1,2, WEI Zongshou1,2, LI Wenyuan1,2, ZHANG Linjing1,2
（1. Automatic Control Research Institute, Lanzhou Jiaotong University, Lanzhou 730070， China；
2. Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070， China）
Abstract：In the strapdown inertial navigation system, the attitude information is obtained through an inertial measurement unit (IMU) device, which mainly includes a triaxial gyroscope, a triaxial accelerometer and a triaxial magnetometer. However, IMU sensors have system noise and drift errors, and these errors can accumulate over time, which makes it difficult to control the attitude accuracy. In order to solve the problems of gyro drift over time and random errors generated by the surrounding environment, this paper presents an attitude calculation algorithm based on wavelet neural network-extended Kalman filter (WNN-EKF). The wavelet neural network (WNN) is used to optimize the model and compensate the extended Kalman filter’s own model error. Through the semi-physical simulation experiment, the results show that the algorithm improves the accuracy of attitude calculation and enhances the self-adaptability to the environment.
Key words：inertial measurement unit (IMU); quaternion; attitude calculation; wavelet neural network (WNN); extended Kalman filter (EKF)
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一种基于WNN-EKF的姿态解算方法
陈光武1,2， 樊子艳1,2， 魏宗寿1,2， 李文元1,2， 张琳婧1,2
（1. 兰州交通大学 自动控制研究所， 甘肃 兰州 730070； 2. 甘肃省高原交通信息工程及控制重点实验室， 甘肃 兰州 730070）
摘 要: 在捷联惯导系统中， 姿态信息通过惯性测量单元 (Inertial measurement unit, IMU) 器件来获取， 主要包含三轴陀螺仪和三轴加速度计。 然而， 由于IMU传感器存在系统噪声、 漂移误差， 且这些误差会随着时间增加而积累， 这使得姿态的精度控制变得困难。 为了解决陀螺随时间漂移以及周围环境产生随机误差的问题， 本文在卡尔曼滤波和神经网络模型的基础上， 提出了一种基于小波神经网络——扩展卡尔曼滤波的姿态解算算法， 对卡尔曼滤波的结果用小波神经网络予以模型优化， 补偿扩展卡尔曼滤波自身存在的模型误差。 半实物仿真实验结果表明， 该算法提高了姿态解算精度， 增强了对环境的自适应能力。
关键词： 惯性测量单元； 四元数； 姿态解算； 小波神经网络； 扩展卡尔曼滤波
引用格式：CHEN Guangwu, FAN Ziyan, WEI Zongshou, et al. An attitude calculation algorithm based on WNN-EKF. Journal of Measurement Science and Instrumentation, 2022， 13（2）：138-146. DOI：10.3969／j.issn.1674-8042.2022.02.002
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