Abstract
De-noising IMU data is an important approach to improve the accuracy of INS. Wavelet threshold de-noising has many limitations under certain conditions, especially in processing inertial sensor errors. Based on Empirical Mode Decomposition (EMD), a novel systematic de-noising methodology for inertial sensor errors is established, including EMD compulsive de-noising, feasible in static conditions, and EMD threshold de-noising, feasible in dynamic conditions. For static data, EMD compulsive de-noising method first disposes IMFs of exceptional noise by 2sigma criterion and then the number of IMFs of high frequency noise is determined by correlation coefficient. The de-noising process is finally done by reconstructing the other IMFs. For dynamic data, EMD threshold de-noising method utilizes fractional Gaussian noise as the model of inertial sensor errors. The model parameter estimation method by power spectral density is given. Noise variance in IMFs is derived and noise thresholds of IMFs are estimated through the obtained variance.
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