摘要
捷联惯导系统中使用的石英振梁加速度计随温度输出漂移比较显著,通过理论分析和试验研究了其静态定点温度特性,提出了在处理受温度影响的加速度计数据时建立基于梯度下降学习算法的小波神经网络模型,并对其进行补偿。通过惯导系统初始对准实验结果表明,该方法与传统的最小二乘法相比,小波神经网络的非线性逼近能力更强,曲线拟合精度更高,能有效补偿加速度计的温度漂移,降低惯导系统初始对准后的姿态解算误差。
Quartz vibrating beam accelerometer used in the strapdown inertial navigation system drifts significantly with the temperature, temperature characteristic, through theoretical we put forward to establish analysis and experimental research on its static point a model of wavelet neural network based on gradient descent algorithm in the treatment of the affected by the temperature of the beam accelerometer data, and to carry on the compensation. Initial alignment by inertial navigation system experiments show that wavelet neural network＇ s nonlinear approximation ability is stronger, and its curve fitting accuracy which can effectively compensate the temperature drift of accelerometer and reduce error algorithm after the initial alignment of inertial navigation system. higher, attitude
出处
《兵器装备工程学报》
CAS
2016年第7期118-122,共5页
Journal of Ordnance Equipment Engineering
关键词
石英振梁加速度计
小波神经网络
温度漂移
姿态解算
quartz vibrating beam accelerometer
wavelet neural network
temperature drift
attitude algorithm
