Frequency estimation of electric signals based on the adaptive short-time Fourier transform

Jiang, Yaqun; He, Yigang

doi:10.1080/00207210802537494

Cited by 5 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The derivation of (21) is complicated, and thus we directly prove (22) by analyzing the TF energy distribution of the linear FM signal. Since the chirp rate is a constant for the linear FM signal, it is reasonable to use σ(t, f ) = σ in (12). The envelope of the corresponding ASTFT-tf is given by…”

Section: Optimal Standard Deviation Of the Gaussian Window Versus mentioning

confidence: 99%

STFT With Adaptive Window Width Based on the Chirp Rate

Pei

Huang

2012

IEEE Trans. Signal Process.

121

View full text Add to dashboard Cite

Abstract-An adaptive time-frequency representation (TFR) with higher energy concentration usually requires higher complexity. Recently, a low-complexity adaptive short-time Fourier transform (ASTFT) based on the chirp rate has been proposed. To enhance the performance, this method is substantially modified in this paper: i) because the wavelet transform used for instantaneous frequency (IF) estimation is not signal-dependent, a low-complexity ASTFT based on a novel concentration measure is addressed; ii) in order to increase robustness to IF estimation error, the principal component analysis (PCA) replaces the difference operator for calculating the chirp rate; and iii) a more robust Gaussian kernel with time-frequency-varying window width is proposed. Simulation results show that our method has higher energy concentration than the other ASTFTs, especially for multicomponent signals and nonlinear FM signals. Also, for IF estimation, our method is superior to many other adaptive TFRs in low signal-to-noise ratio (SNR) environments.

show abstract

Section: Optimal Standard Deviation Of the Gaussian Window Versus mentioning

confidence: 99%

STFT With Adaptive Window Width Based on the Chirp Rate

Pei

Huang

2012

IEEE Trans. Signal Process.

121

View full text Add to dashboard Cite

show abstract

“…The window used in STFT provides the fixed resolution in TF domain [17]. The adaptive STFT (ASTFT) techniques have been proposed to overcome the resolution issue of STFT in [18,19]. It should be noted that these ASTFT techniques are computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

Sharma

Pachori

2018

IET Science, Measurement & Technology

115

View full text Add to dashboard Cite

“…Using L'Hopital's rule and that , we can find for for otherwise (10) which is an times weighted Kronecker delta function for and is similar to an weighted Kronecker delta function when is close to 0 or close to . Since the phase change of due to the change in , i.e., , is found as (11) the difference in results in a phase difference in from 0 to . Therefore, since (8c) is a homogeneous system, the phase difference of for neighboring values depends only on .…”

Section: A Proposed Forward Stft Enhancement Techniquementioning

confidence: 99%

“…To adjust the time-frequency resolution appropriately to time-varying signals, a number of adaptive STFT (ASTFT) techniques have been introduced in the literature [8]- [11], most of which can be classified into two groups; one is the concentration measure (CM)-based, and the other is chirp-rate (CR)-based [7]. In the CM-based ASTFT, the effects of certain parameter variations on the energy concentration of the input signal is examined in the time-frequency domain to find the optimum parameter value, yielding the highest energy concentration, which is used to perform STFT [12], [13].…”

mentioning

confidence: 99%

Low Computational Enhancement of STFT-Based Parameter Estimation

Kim

Kong

Kim

2015

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Short-time Fourier transform (STFT) is one of the most widely used tools to analyze frequency and phase of local sections of time-varying signals using a time window function. Since the fixed window size of STFT causes limitation in time-frequency representation (TFR), adaptive STFT (ASTFT) techniques have been studied to adjust the window size depending on the local signal characteristics. However, ASTFT techniques suffer from heavy computational complexity in general. In this paper, we propose low computational enhanced temporal and spectral parameter estimation techniques for STFT output of radar signals; forward and backward STFT enhancement techniques. The proposed forward STFT enhancement technique is to enhance the spectral resolution of a received radar signal by multiple times using a number of successive STFT outputs without applying additional STFT with wider time window. On the other hand, the proposed backward STFT enhancement technique is to improve the temporal resolution of a received radar signal using an STFT output with a wide time window, without applying multiple STFTs with smaller time window. The performance of the proposed techniques are theoretically analyzed, and the advantage of the proposed techniques to the conventional techniques in terms of computational complexity is demonstrated with numerous simulations.

show abstract

Frequency estimation of electric signals based on the adaptive short-time Fourier transform

Cited by 5 publications

References 20 publications

STFT With Adaptive Window Width Based on the Chirp Rate

STFT With Adaptive Window Width Based on the Chirp Rate

Time–frequency representation using IEVDHM–HT with application to classification of epileptic EEG signals

Low Computational Enhancement of STFT-Based Parameter Estimation

Contact Info

Product

Resources

About