An Improved Multivariate Synchrosqueezing Wavelet Transform Denoising Method Using Subspace Projection

In recent years, the development of multi-sensor has emphasized the need to directly process multi-channel (multivariate) data. In this paper, a novel multivariate synchrosqueezing wavelet transform denoising method combined with subspace projection (SWT-SP) is proposed. One of the key points of this method is to obtain an optimal orthogonal matrix which can project a multivariate observation signal to a signal subspace occupied by a clean signal and an orthogonal noise subspace occupied by noise. Furthermore, the high dimensional time-frequency representation based on the synchrosqueezing transform realizes the multichannel signal information fusion, and the subspace projection makes full use of the spatial diversity characteristics of the observed signal. Finally, signal energy produces the aggregation effect in the former dimension space, which improves the signal-to-noise ratio(SNR) of signals in the signal subspace. The performance of this algorithm for standard multichannel denoising is verified on both real-world data and synthetic signals. The reconstructed signal obtained the improvement of the highest SNR by about 6 dB under different conditions.