Discrete evolutionary transform for time–frequency signal analysis

Suleesathira, Raungrong; Chaparro, Luıs F.; Akan, Aydın

doi:10.1016/s0016-0032(00)00041-7

Cited by 40 publications

(25 citation statements)

References 16 publications

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“…Recently, the time-frequency analysis methods have introduced a joint time-frequency energy distribution plane displaying the jointly both time and frequency information. Time-frequency distribution methods who do not show cross terms or negative frequency like STFT and the Discrete Evolutionary Transform (DET) provide a time-frequency distribution plane that is positive and has no cross term as well [8][9][10].…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

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Non-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane

Alshehri¹

2012

JSIP

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Multi-components sinusoidal engineering signals who are non-stationary signals were considered in this study since their separation and segmentations are of great interests in many engineering fields. In most cases, the segmentation of non-stationary or multi-component signals is conducted in time domain. In this paper, we explore the advantages of applying joint time-frequency (TF) distribution of the multi-component signals to identify their segments. The Spectrogram that is known as Short-Time Fourier Transform (STFT) will be used for obtaining the time-frequency kernel. Time marginal of the computed kernel is optimally used for the signal segmentation. In order to obtain the desirable segmentation, it requires first to improve time marginal of the kernel by using two-dimensional Wiener mask filter applied to the TF kernel to mitigate and suppress non-stationary noise or interference. Additionally, a proper choice of the sliding window and its overlaying has enhanced our scheme to capture the discontinuities corresponding to the boundaries of the candidate segments.

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Section: Short-time Fourier Transformmentioning

confidence: 99%

“…In STFT, time-localization is a achieved first by windowing the signal by cutting off a slice of it and then taking its Fourier Transform using Fast Fourier Transform (FFT) [9,10]. The magnitude of the STFT kernel is known as the Spectrogram.…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

Non-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane

Alshehri¹

2012

JSIP

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“…Priestley's evolutionary spectrum [11], [14] of γ (n) is given as the magnitude square of the evolutionary kernel (n, ω). Analogous to the above Wold-Cramer representation, a discrete, time-frequency representation for a deterministic signal x(n) with a time-dependent spectrum is possible [1], [22]:…”

Section: The Discrete Evolutionary Transformmentioning

confidence: 99%

“…The DET is obtained by expressing the kernels X (n, ω k ) or X ( s , ω k ) directly from the signal [22]. Thus, for the representation in (12), the DET that provides the evolutionary kernel X (n, ω k ), 0 ≤ k ≤ K − 1, is given by…”

Section: The Discrete Evolutionary Transformmentioning

confidence: 99%

Modeling and Estimation of Wireless OFDM Channels by Using Time-Frequency Analysis

Akan

Chaparro

2006

Circuits Syst Signal Process

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Orthogonal frequency division multiplexing (OFDM) has become a very popular method for high data rate wireless communications because of its advantages over single carrier modulation schemes on multipath, frequency selective fading channels. However, intercarrier interference, due to Doppler frequency shifts, and multipath fading severely degrade the performance of OFDM systems. Estimation of channel parameters is required at the receiver. In this paper, we present a channel modeling and estimation method based on the time-frequency representation of the received signal. The discrete evolutionary transform provides a time-frequency procedure to obtain a complete characterization of the multipath, fading, and frequency selective channels. Simulations are used to illustrate the performance of the proposed procedure and to compare it with other time-varying channel estimation techniques.

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“…In addition to Priestley, estimation of the ES has been condidered by many others [12], [17], [18], [22], [30], [31], [32]. (There have also been generalizations and modifications of the ES [1], [11], [34] directed to the analysis of deterministic signals and involving instantaneous frequency. )…”

Section: Introductionmentioning

confidence: 99%

Statistical Properties for Coherence Estimators From Evolutionary Spectra

Walden

Cohen

2012

IEEE Trans. Signal Process.

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Abstract-Evolutionary spectra were developed by Priestley to extend spectral analysis to some nonstationary time series, in particularly semistationary processes, of which the ubiquitous uniformly modulated processes are a subclass. Coherence is well-defined for bivariate semistationary processes and can be estimated from such processes. We consider Priestley's estimator for the evolutionary spectral density matrix, and show that its elements can be written as weighted multitaper estimators with calculable weights and tapers. Under Gaussianity an approximating Wishartdistribution model follows for the spectral matrix, valid for all frequencies except small computable intervals near zero and Nyquist. Moreover, the critically important degrees of freedom are known. Consequently the statistical distribution of the coherence is given by Goodman's distribution and the raw coherence estimate can be accurately debiased. Theoretical results are verified using a model for wind fluctuations: simulations give excellent agreement between the mean debiased coherence estimates and true coherence, and between the proposed and empirical distributions of coherence.

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Discrete evolutionary transform for time–frequency signal analysis

Cited by 40 publications

References 16 publications

Non-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane

Non-Stationary Signal Segmentation and Separation from Joint Time-Frequency Plane

Modeling and Estimation of Wireless OFDM Channels by Using Time-Frequency Analysis

Statistical Properties for Coherence Estimators From Evolutionary Spectra

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