Noise reduction for high-accuracy automatic calibration of resolver signals via DWT-SVD based filter

High-accuracy calibration of resolver signals is the key to improve its angular measurement accuracy. However, inductive harmonics, residual excitation components, and random noise in signals dramatically restrict the further improvement of calibration accuracy. Aiming to reduce these unexpected noises, a filter based on discrete wavelet transform (DWT) and singular value decomposition (SVD) is designed in this paper. Firstly, the signal was decomposed into a time-frequency domain by DWT and several groups of coefficients were obtained. Next, the SVD operation of a Hankel matrix created from the coefficients was made. Afterwards, the noises were attenuated by reconstructing the signal with a few selected singular values. Compared with a conventional low-pass filter, this method can almost only preserve the fundamental and DC components of the signal because of the multi-resolution characteristic of DWT and the good correspondence between the singular value and frequency. Therefore, the calibration accuracy of the imperfect characteristics could be improved effectively. Simulation and experimental results demonstrated the effectiveness of the proposed method.