Adaptive Instantaneous Frequency Estimation of Multicomponent Signals Based on Linear Time–Frequency Transforms

Abdoush, Yazan; Pojani, Giacomo; Corazza, G.E.

doi:10.1109/tsp.2019.2912132

Cited by 32 publications

(16 citation statements)

References 31 publications

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“…Authors in [45,47,48], have proposed variants of ASTFT which adapt the window width based on the chirp rate of the signal (first derivative of the IF). First, a preliminary TFR (PTFR) is constructed to make rough IF estimate by detecting the ridges of the TFR, and thereafter the chirp rate, defined as the first derivative of the IF, is computed.…”

Section: Application Of Owfs In Time-frequency Analysismentioning

confidence: 99%

“…, where ν 0 represents regular rate [48] and N is the length of the signal. Through simulation, it is found that >99% of energy is captured within a TF box of area BM = 2/N.…”

Section: Application Of Owfs In Time-frequency Analysismentioning

confidence: 99%

“…Herein, this variant is represented as ASTFT‐tf. It is mentioned in [48] that in case of multicomponent signal and in noisy environment, the CM‐based methods favour the slow‐varying frequency component compared to the fast‐varying component. Therefore, to obtain the PTFR, the authors used the regular rate which is the ratio between the effective bandwidth and effective time duration of the signal.…”

Section: Application Of Owfs In Time–frequency Analysismentioning

confidence: 99%

“…For calculating the chirp rate, PCA is used in ASTFT‐t, GSTFT‐tf, and OASTFT‐tf as it is less sensitive to IF estimation error. In the case of OASTFT‐tf, the relationship between the regular rate and window width parameters M and B can be represented as

true \frac{B}{M} = \frac{ν_{0}}{2 N}

, where

ν_{0}

represents regular rate [48] and N is the length of the signal. Through simulation, it is found that >99% of energy is captured within a TF box of area

B M = 2 / N

.…”

Section: Application Of Owfs In Time–frequency Analysismentioning

confidence: 99%

See 3 more Smart Citations

Achievable simultaneous time and frequency domain energy concentration for finite length sequences

Singh

Pradhan

2019

IET signal process.

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For numerous applications in the field of signal processing, it is desired to design a compact window that can simultaneously concentrate maximum energy in finite time interval and frequency band. Although zero-order discrete prolate spheroidal sequence (DPSS) meets this requirement, it is of infinite support. Limiting this sequence to finite support no longer guarantees the optimality property. This study aims at designing a discrete finite length window that can maximise the energy simultaneously in narrow time interval and frequency band. A multi-objective optimisation approach is adopted to obtain the upper bound of maximum achievable time and frequency domain energy concentrations for finite length sequences. The optimal sequence thus obtained is termed as the optimal window with finite support (OWFS), and its various associated properties are discussed. It is shown analytically that as the support of OWFS approaches infinity, it converges to zero-order DPSS. In order to illustrate its optimality, the compactness of the proposed OWFS is compared with those of various window functions. Extending the proposed OWFS, this study also discusses the formulation and associated properties of the zero-order periodic DPSS. Further, the closed form expression for the upper bound of achievable time and frequency domain energy concentrations is also derived.

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Section: Application Of Owfs In Time-frequency Analysismentioning

confidence: 99%

“…, where ν 0 represents regular rate [48] and N is the length of the signal. Through simulation, it is found that >99% of energy is captured within a TF box of area BM = 2/N.…”

Section: Application Of Owfs In Time-frequency Analysismentioning

confidence: 99%

Section: Application Of Owfs In Time–frequency Analysismentioning

confidence: 99%

true \frac{B}{M} = \frac{ν_{0}}{2 N}

, where

ν_{0}

represents regular rate [48] and N is the length of the signal. Through simulation, it is found that >99% of energy is captured within a TF box of area

B M = 2 / N

.…”

Section: Application Of Owfs In Time–frequency Analysismentioning

confidence: 99%

See 2 more Smart Citations

Achievable simultaneous time and frequency domain energy concentration for finite length sequences

Singh

Pradhan

2019

IET signal process.

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show abstract

“…Recently, time-frequency (TF) analysis (TFA) methods have attracted considerable attention in instantaneous frequency (IF) estimation for instantaneous rotational speed estimation [11][12][13], as they can provide a powerful tool for the analysis of time series signals and produce more reliable and more robust results [14]. Because the IF of nonstationary signal is the ridge point in the TF representation, the speedrelated frequency can be estimated by detecting the ridge of the TF representation of vibration signals [15][16][17][18][19][20][21][22][23]. Urbanek et al [24] utilize phase demodulation and short-time Fourier transform (STFT) to obtain the IF that is related to the speed.…”

Section: Introductionmentioning

confidence: 99%

An Instantaneous Engine Speed Estimation Method Using Multiple Matching Synchrosqueezing Transform

et al. 2021

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Instantaneous rotational speed measurement of the engine is crucial in routine inspection and maintenance of an automobile engine. Since the contact measurement of rotational speed is not always available, the vibration measurement has been used for noncontact rotational speed estimation methods. Unfortunately, the accuracy of the noncontact estimation methods by analyzing engine vibration frequency is not satisfactory due to the influence of noise and the strong nonstationary characteristic of the vibration signal. To overcome these problems, based on the multiple matching synchrosqueezing transform (MSST) (MMSST, improved MSST with multiple squeeze operations), a novel noncontact method is proposed to accurately estimate the instantaneous rotational speed of automobile engine in this paper. Firstly, a MMSST is proposed to process the vibration signal to obtain a concentrated time-frequency (TF) representation. Secondly, the instantaneous frequency (IF) detection algorithm is employed to extract the fundamental frequency from the TF result. Finally, the rotational speed of the engine is calculated according to the relationship between the fundamental frequency and rotational speed. Results from numerical simulations and test on real engine have proven that the proposed method can obtain much higher frequency resolution and more precise IF estimation of the engine vibration signal and more accurate rotational speed estimation result compared with the MSST method. Furthermore, the proposed method is verified to have a stronger noise robustness and can provide satisfactory estimation results for engine vibration signal containing nonlinear frequency-modulated components.

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Sparse time‐frequency analysis for aircraft target classification with low sampling rate and short observation time

Wang

Yang

Yin

et al. 2021

Int J Numerical Modelling

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The traditional time‐frequency analysis (TFA) techniques are instruments for target classification, which can reflect the feature of the target in the time‐frequency domain. However, it will lead to a serious decrease in the recognition accuracy as the decline of the sampling rate. To ease this problem, in this article, the sparse time‐frequency feature analysis (STFFA) is implemented for aircraft classification, and the genetic algorithm is adopted to solve the sparse problem quickly for saving time. Firstly, the sparse time‐frequency decomposition and recovery signal are obtained by matching pursuit. Then, three novel kinds of aircraft features are extracted from the sparse time‐frequency decomposition, which are sparse recovery time‐frequency entropy (SRTFE), frequency entropy by time, and first‐order sparse time‐frequency moment. Thus, the feature combination modes based on the three features are applied to realize the classification of aircraft and compared with the traditional TFA techniques. Besides, a support vector machine is also used to classify the three kinds of aircraft. The accuracy and efficiency of the STFFA method have been investigated by employing the parametric model data and electromagnetic scattering model simulated trials. Furthermore, in contrast to the traditional TFA instrument, our method can reach a recognition accuracy of more than 90% from the numerical experiment results, which demonstrates that the feature extraction by sparse time‐frequency analysis improves the accuracy of aircraft classification under a low sampling rate.

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Adaptive Instantaneous Frequency Estimation of Multicomponent Signals Based on Linear Time–Frequency Transforms

Cited by 32 publications

References 31 publications

Achievable simultaneous time and frequency domain energy concentration for finite length sequences

Achievable simultaneous time and frequency domain energy concentration for finite length sequences

An Instantaneous Engine Speed Estimation Method Using Multiple Matching Synchrosqueezing Transform

Sparse time‐frequency analysis for aircraft target classification with low sampling rate and short observation time

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