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Time–frequency analysis

About: Time–frequency analysis is a research topic. Over the lifetime, 5407 publications have been published within this topic receiving 104346 citations.


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Journal ArticleDOI
TL;DR: This paper proposes an IF estimation algorithm by exploiting the combination of TF distributions (TFDs) and image processing techniques, andumerical results are provided to demonstrate desirable capabilities of the proposed algorithm.
Abstract: This paper addresses the problem of estimating the instantaneous frequency (IF) of multicomponent radar signals, which are assumed to be frequency-modulated (FM). The difficulties lie in that different FM components which overlap in time-frequency (TF) domain have different time-supports, and their spectral contents may present continuous and stepped IF laws. We propose an IF estimation algorithm by exploiting the combination of TF distributions (TFDs) and image processing techniques. Numerical results are provided to demonstrate desirable capabilities of the proposed algorithm.

56 citations

Journal ArticleDOI
TL;DR: In this article, various joint time-frequency distributions (TFDs) can optimize the trade-off between time resolution and frequency resolution for spectroscopic optical coherence tomography (SOCT) signals.
Abstract: The analysis of spectroscopic optical coherence tomography (SOCT) signals suffers the trade-off between time resolution and frequency resolution. Various joint time–frequency distributions (TFDs) can optimize this trade-off. Synthesized signals were generated and experimentally acquired data were obtained to compare and validate several different TFDs under different SOCT imaging schemes. Specific criteria were designed to quantify the TFD performance. We found that different SOCT imaging schemes require different optimal TFDs. Cohen’s class TFDs generate the most compact time–frequency (TF) analysis, while linear TFDs offer the most reliable TF analysis. In both cases, if some prior information is known, model-based TF analysis can improve the performance.

56 citations

Journal ArticleDOI
TL;DR: The resulting multidirectional distribution (MDD) approach proves to be more effective than classical methods like extended modified B distribution, S-method, or compact kernel distribution in terms of auto-terms resolution and cross-terms suppression.
Abstract: This paper presents a new advanced methodology for designing high resolution time–frequency distributions (TFDs) of multicomponent nonstationary signals that can be approximated using piece-wise linear frequency modulated (PW-LFM) signals. Most previous kernel design methods assumed that signals auto-terms are mostly centered around the origin of the $( u,\tau)$ ambiguity domain while signal cross-terms are mostly away from the origin. This study uses a multicomponent test signal for which each component is modeled as a PW-LFM signal; it finds that the above assumption is a very rough approximation of the location of the auto-terms energy and cross-terms energy in the ambiguity domain and it is only valid for signals that are well separated in the $(t,f)$ domain. A refined investigation led to improved specifications for separating cross-terms from auto-terms in the $( u,\tau)$ ambiguity domain. The resulting approach first represents the signal in the ambiguity domain, and then applies a multidirectional signal dependent compact kernel that accounts for the direction of the auto-terms energy. The resulting multidirectional distribution (MDD) approach proves to be more effective than classical methods like extended modified B distribution, S-method, or compact kernel distribution in terms of auto-terms resolution and cross-terms suppression. Results on simulated and real data validate the improved performance of the MDD, showing up to 8% gain as compared to more standard state-of-the-art TFDs.

56 citations

Journal ArticleDOI
TL;DR: The proposed VSLCT is an extended version of the current linear transform that can effectively alleviate the smear effect and can dynamically provide desirable time–frequency resolution in response to condition variations.
Abstract: Linear transform has been widely used in time–frequency analysis of rotational machine vibration. However, the linear transform and its variants in current forms cannot be used to reliably analyze rotational machinery vibration signals under nonstationary conditions because of their smear effect and limited time variability in time–frequency resolution. As such, this paper proposes a new time–frequency method, named velocity synchronous linear chirplet transform (VSLCT). The proposed VSLCT is an extended version of the current linear transform. It can effectively alleviate the smear effect and can dynamically provide desirable time–frequency resolution in response to condition variations. The smearing problem is resolved by using linear chirplet bases with frequencies synchronous with shaft rotational velocity, and the time–frequency resolution is made responsive to signal condition changes using time-varying window lengths. To successfully implement the VSLCT, a kurtosis-guided approach is proposed to dynamically determine the two time-varying parameters, i.e., window length and normalized angle. Therefore, the VSLCT does not require the user to provide such parameters and hence avoids the subjectivity and bias of human judgment that is often time-consuming and knowledge-demanding. This method can also analyze normal monocomponent frequency-modulated signal.

56 citations

Journal ArticleDOI
TL;DR: An algorithm based on Hilbert-Huang Transform (HHT) is developed to compute statistically significant time-frequency spectra of T MS-evoked EEG oscillations on a single trial basis and it is found that the HHT-based algorithm outperforms the WT-based one in detecting the time onset of TMS- Evoked oscillations in the classical EEG bands.

55 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023164
2022338
2021253
2020229
2019261
2018320