浮球平台光纤陀螺数据实时降噪方法研究

Research on Real-time De-noising of Floated Inertial Platform System FOG Data

以浮球式惯性平台上的光纤陀螺为研究对象,针对其输出原始数据噪声较大的问题,分别利用LMS自适应滤波算法、改进的小波实时降噪对光纤陀螺原始数据进行实时滤波处理。利用Matlab进行了陀螺数据模拟实时降噪实验,并对两种方法的降噪效果进行比较,结果表明两种方法对原始数据降噪都有明显效果,改进的小波实时降噪效果要优于LMS自适应算法。

The fiber optic gyro （FOG） in the floated inertial platform system is regarded as the object of study. Aiming at the problem of the noisy original output data for FOG, LMS adaptive filtering algorithm and improved real-time de-noising wavelet were used to do the real-time filter of FOG original data by Matlab. Compared with the performance of the two methods, the experiment results show that both methods enhance the de-noising performances and the improved real-time de-noising wavelet is better than the LMS adaptive filtering algorithm.