基于小波分析的惯性传感器信号Kalman滤波

Kalman Filtering Based on Wavelet Analysis for Signals of Inertial Sensors

针对光电跟踪系统惯性传感器信号特点,本文提出通过小波分析的方式确定相关Kalman滤波的模型及参数。该方法利用小波分析的优良特性,采用先将信号进行去噪处理,然后对去噪后的信号进行AR建模。根据小波去噪后的信号比较接近真实信号,将得到的观测噪声方差乘以一个小于1的系数后作为系统的过程噪声方差,从而确定模型的噪声参数。仿真实验结果表明,该方法不仅对惯性传感器的静态数据有很好的效果,而且对其动态观测数据也有良好的效果。同时,该方法不仅对光电跟踪系统有效,而且还具有一定的通用性。

According to the signal characteristics of inertial sensors in optoelectronic tracking system, an improved Kalman filtering method was designed, by which the AR model of denoised signals was built up and parameters were estimated after the signals were filtered by wavelet. Because the signals filtered by wavelet is approximative to actual ones, the variance of system noises was obtained by the variance of observation noises multiplying a coefficient less than 1. Finally, the experiments about real signals of some inertial sensors verify that the algorithm is efficient and has some catholicity.