Abstract
This paper presents efficient algorithms for the analysis of nonstationary multicomponent signals based on modified local polynomial time-frequency transform. The signals to be analyzed are divided into a number of segments and the desired parameters for computing modified the local polynomial time-frequency transform in each segment are estimated from polynomial Fourier transform in the frequency domain. Compared to other reported algorithms, the length of overlap between consecutive segments is reduced to minimize the overall computational complexity. The concept of adaptive window lengths is also employed to achieve a better time-frequency resolution for each component. Numerical simulations with synthesized multicomponent signals show that the proposed ones achieve better performance on instantaneous frequency estimation with greatly reduced computational complexity.
Highlights
Due to their superior performance in dealing with nonstationary signals, time-frequency transforms (TFTs) have found various applications in many areas including communications, multimedia, mechanics, and biology [1]
Local polynomial time-frequency transform (LPTFT), referred to as the generalization of short-time Fourier transform (STFT), was reported to provide high resolution for nonstationary signals [3, 4] with a local polynomial function approximating to the frequency characteristics
The second-order MLPTFTp is used in all experiments dealing with the input sequence x(t) with N = 512
Summary
Due to their superior performance in dealing with nonstationary signals, time-frequency transforms (TFTs) have found various applications in many areas including communications, multimedia, mechanics, and biology [1]. The estimation of a number of extra parameters required by LPTFT computation results in a heavy computational load. This is mainly due to the long overlap between consecutive signal segments for which the estimation process is implemented [4]. This paper presents analysis algorithms for time-varying multicomponent signals containing white Gaussian and/or impulse noises. Different from previously reported algorithms, the proposed modified local polynomial timefrequency transform (MLPTFT) reduces the overlap length between consecutive segments to minimize the number of segments to be processed. Heavy computational complexity is needed for estimating the extra parameters required by LPTFT computation if the overlap length is large because the number of signal segments to be processed is increased
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