Abstract
In this paper, a novel time–frequency (TF) analysis method, called the short-time Fourier transform using odd symmetric window function (OSTFT), is proposed using odd symmetric window function to replace the conventional even window function of STFT. Different from conventional STFT acquiring the amplitude maximum at time and frequency centers, OSTFT acquires the minimum amplitude of 0. Hence, OSTFT can obtain a TF representation (TFR) with high TF resolution by utilizing the leaked energy rather than restraining it. It is worth to mention that the proposed OSTFT can vitiate the effect of window size we choose on the TFR obtained. Furthermore, it also has a good performance on signals with complex instantaneous frequencies (IFs), even crossing IFs. Because we just replace the conventional window function of STFT, the time-consuming of the proposed OSTFT is at the same level as the conventional STFT. The effectiveness of proposed OSTFT has been validated on two complex multi-component simulated numerical signals and a signal collected from the brown bat.
Highlights
Because of the wide existence of nonstationary signals, the TF analysis methods on analyzing these signals, such as the short-time Fourier transform (STFT) [1], the Wigner-Ville distribution (WVD) [2] and the continuous wavelet transform (CWT) [3], have been developed for a long time
It is worth to mention that the proposed odd symmetric window function (OSTFT) can vitiate the effect of window size that we choose on the time-frequency representation (TFR) obtained
One is the effect of the parameter for window size we choose on the TFR obtained using OSTFT
Summary
Because of the wide existence of nonstationary signals, the TF analysis methods on analyzing these signals, such as the short-time Fourier transform (STFT) [1], the Wigner-Ville distribution (WVD) [2] and the continuous wavelet transform (CWT) [3], have been developed for a long time. To improve the smear effect, the velocity synchronous linear CT (VSLCT) was proposed by constructing the basis according to the rotating speed to make the chirplets match the IFs better [11] In this way, VSLCT can obtain a TFR with high time-frequency resolution. It is a good idea to vitiate the bad effect of the uncertainty principle by utilizing an adaptive window size It performs not very well on analyzing the signals with the crossing IFs. For the decomposition-based methods, the core idea of these methods is to decompose the multicomponent signal into a series of mono-component signals without constructing basis function in advance.
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