提升小波变换在HRG动态信号处理中的应用  

Application of Lifting Wavelet Transform in HRG Dynamic Signal Processing

作　　者：朱战辉 汪立新[1] 

机构地区：[1]第二炮兵工程大学,西安710025

出　　处：《电光与控制》2015年第8期29-32,共4页Electronics Optics & Control

基　　金：国防预研基金(103030203);校创新基金(XY2013JJB20)

摘　　要：为减少动态环境下半球谐振陀螺输出信号噪声,提高惯导系统精度,提出了一种基于提升小波变换(LWT)的滤波算法。将信号经小波变换提升算法进行尺度分解后,对低频小波系数进行前向线性预测(FLP)滤波,进一步去除低频干扰,提取有用信号,高频小波系数直接置零或按照一定阈值规则处理,显著提高了重构信号的精度,去除了陀螺噪声。详细对比了新算法、卡尔曼滤波算法和FLP滤波算法,并分析了三者的去噪效果。仿真结果和实际试验数据表明:新算法能有效抑制动态环境下半球谐振陀螺输出噪声,标准差为卡尔曼滤波算法的一半,运算时间缩短了30%;该滤波算法不但速度快,精度高,而且计算量小,抗干扰性好,滤波后可以有效跟踪有用信号。To reduce the output signal noise of Hemispherical Resonator Gyro（ HRG） in dynamic environment and improve the precision of inertial navigation system, a filtering algorithm based on Lifting Wavelet Transform（ LWT） is proposed. The signal is decomposed with wavelet transform lifting algorithm, and then the lowfrequency wavelet coefficient is processed with Forward Linear Prediction（ FLP） filter, to further remove the low-frequency interference and extract the desired signal. The high-frequency wavelet coefficient is directly to be zeroed or processed according to certain threshold rules, which significantly improves the accuracy of reconstructed signal, and realizes removal of the gyro noise. A comparison of the new algorithm, Kalman filtering algorithm and FLP filtering algorithm is made in detail, as well as an analysis of their de-noising effect. It＇s verified by the simulation results and actual test data that the new algorithm can effectively reduce the output noise of HRG in dynamic environment, the standard deviation is reduced by about 50% and the operation time is shortened by 30% as compared with Kalman filtering. This filtering algorithm is not only of fast speed and high accuracy, but also with a small amount of calculation and good anti-interference performance, enabling to effectively track the desired signal after filtering.

关 键 词：半球谐振陀螺 提升小波变换 前向线性预测滤波 融合算法 动态信号 

分 类 号：V271.4[航空宇航科学与技术—飞行器设计] U241.5[交通运输工程—道路与铁道工程]