An Improved Design of High-Resolution Quadratic Time–Frequency Distributions for the Analysis of Nonstationary Multicomponent Signals Using Directional Compact Kernels

Boashash, Boualem; Ouelha, Samir

doi:10.1109/tsp.2017.2669899

Cited by 58 publications

(10 citation statements)

References 27 publications

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“…In this study, we adopt the short‐time Fourier transform (STFT) in the algorithm, which is widely used and has no cross‐terms (as shown in (10)). Obviously, it can be easily rewritten using other reversible T‐F representations (TFRs), but the TFR needs to be able to suppress cross‐terms effectively and have high T‐F resolution, such as extended modified B distribution, multidirectional distribution [27]

\begin{matrix} 1em4pt \\ S ()(, t, f) = \int_{- normal∞}^{+ normal∞} s_{r} ()(, τ) w_{h}^{*} ()(, t - τ) \exp ()(, - normalj 2 π f τ) normald τ \end{matrix}

where

S ()(, t, f)

is the obtained TFR,

w_{h} ()(, t)

is a Gaussian window with a length of h .…”

Section: Short Time Variational Mode Decompositionmentioning

confidence: 99%

Time‐based multi‐component irregular FM micro‐Doppler signals decomposition via STVMD

Xia

Dong

2020

IET Radar, Sonar & Navigation

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\begin{matrix} 1em4pt \\ S ()(, t, f) = \int_{- normal∞}^{+ normal∞} s_{r} ()(, τ) w_{h}^{*} ()(, t - τ) \exp ()(, - normalj 2 π f τ) normald τ \end{matrix}

where

S ()(, t, f)

is the obtained TFR,

w_{h} ()(, t)

is a Gaussian window with a length of h .…”

Section: Short Time Variational Mode Decompositionmentioning

confidence: 99%

Time‐based multi‐component irregular FM micro‐Doppler signals decomposition via STVMD

Xia

Dong

2020

IET Radar, Sonar & Navigation

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“…For Algorithm 1, when ∆f k < ∆f, ∆fm < ∆f and the amplitude of point B is greater than that of point C, the frequency of point B will be considered as the frequency of component k, so components k and m cannot be accurately identified. The peak frequencies of points B and C can be obtained by formula (4). We can compute the minimum distance between them and the frequency of time instant of point A.…”

Section: : Else 11mentioning

confidence: 99%

“…In many research fields, such as radar, sonar, and mechanical engineering, the signals received by sensors which have time-varying frequency contents are always multicomponent nonstationary signals. [1][2][3][4][5] Therefore, it is necessary to separate components from the signal in order to effectively analyze them, some of which may overlap in the time-frequency domain (TFD).…”

Section: Introductionmentioning

confidence: 99%

An improved separation method of multi-components signal for sensing based on time-frequency representation

Cheng

Shao

Zhao

et al. 2019

Review of Scientific Instruments

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“…It is useful to compare the performance of different TFDs, but it cannot define the performance difference among those TFDs. The normalized instantaneous resolution (NIR) aims to mitigate the TF measures pitfalls [32], [33]. However, it demands accurate detection for the signal components and a maximum of two components at any time instant with approximately equal amplitudes [34].…”

Section: Introductionmentioning

confidence: 99%

Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison

Al-Sa'D

Boashash

Gabbouj

2021

IEEE Trans. Signal Process.

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This paper proposes a new performance evaluation process for time-frequency distributions (TFD) by designing a reference optimal TFD and novel accuracy and resolution measures. The motivation comes from the need for a TFD performance evaluation method that is objective, capable of quantifying the TFD accuracy and resolution, can determine the performance difference among different TFDs, and suitable for signals with an arbitrary number of components, instantaneous frequency and amplitude. We formulate the proposed optimal TFD, namely the piece-wise spline Wigner-Ville distribution (PW-WVD), by decomposing a standard non-stationary signal model using piece-wise linear frequency modulated (LFM) basis and by exploiting the Wigner-Ville distribution optimality for LFM signals. We compare the designed PW-WVD to conventional optimal TFDs and show that the former is more suitable to serve as a reference for TFD performance evaluation. Using the PW-WVD we derive TFD accuracy and resolution measures, compare them to conventional approaches, and analyze their sensitivity to form a TFD selection criteria. We evaluate the accuracy and resolution of twelve different TFDs and develop precise TFD selection strategies with or without prior information on the signal parameters. Results indicate that the compact kernel distribution is the best performing TFD given no prior information on the signal parameters and different TFDs must be selected upon the availability of prior information.

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An Improved Design of High-Resolution Quadratic Time–Frequency Distributions for the Analysis of Nonstationary Multicomponent Signals Using Directional Compact Kernels

Cited by 58 publications

References 27 publications

Time‐based multi‐component irregular FM micro‐Doppler signals decomposition via STVMD

Time‐based multi‐component irregular FM micro‐Doppler signals decomposition via STVMD

An improved separation method of multi-components signal for sensing based on time-frequency representation

Design of an Optimal Piece-Wise Spline Wigner-Ville Distribution for TFD Performance Evaluation and Comparison

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