Energy concentration enhancement using window width optimization in S transform

Pei, Soo‐Chang; Wang, Pai-Wei

doi:10.1109/icassp.2010.5495742

Cited by 13 publications

(15 citation statements)

References 8 publications

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“…The FFT-based implementations of the proposed ASTFT are also introduced. Simulation results show that our method outperforms the CM-based ASTFT [16] and the chirp-ratebased ASTFT [13] in both noiseless and noisy environments. For IF estimation based on TFRs, it is shown that our method is superior to many other adaptive TFRs at low signal-tonoise ratio (SNR) but inferior to the adaptive bilinear TFRs at high SNR.…”

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confidence: 93%

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STFT With Adaptive Window Width Based on the Chirp Rate

Pei

Huang

2012

IEEE Trans. Signal Process.

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112

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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.

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confidence: 93%

“…However, the main disadvantage of the CM approach is the very high computational complexity. The CM approach has been used in various TFRs such as the STFT [23], the Stransform [15], [16], the S-method (SM) [24], and the SPWVD [22]. For each TF point, the reassignment methods calculate the center of gravity of the signal energy around this TF point.…”

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confidence: 99%

STFT With Adaptive Window Width Based on the Chirp Rate

Pei

Huang

2012

IEEE Trans. Signal Process.

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112

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“…When higher spectral resolution is needed for the spectral components (i.e., 's) of interest, column-wise circular convolution between and are performed. Denoting as the computed using , is expressed with the -point row-wise FFT for each of interests as (13) The relationship between and is found as (14) The performance of the No-method and the proposed forward STFT enhancement technique is briefly compared in Fig. 5.…”

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]. Therefore, the CM-based ASTFT can produce useful results, however, the process to find the optimum parameter value is computationally expensive.…”

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confidence: 99%

Low Computational Enhancement of STFT-Based Parameter Estimation

Kim

Kong

Kim

2015

IEEE J. Sel. Top. Signal Process.

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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.

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“…In [13]- [14], this has been performed through the introduction and the optimal selection of an extra parameter in the standard Stransform relation. The methods proposed in [15]- [16] are based on optimal selection of Gaussian window parameters, considering a concentration measure. Since hyperbolic window has three parameters which define its shape in addition to its width, the optimal selection of hyperbolic window parameters is trickier in comparison to a Gaussian window which has only one parameter.…”

Section: Introductionmentioning

confidence: 99%

Optimal selection of analyzing window of arbitrary shape for S-transform using PSO algorithm

Ashrafian

Mirsalim

Suratgar

2014

2014 22nd Iranian Conference on Electrical Engineering (ICEE)

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In this paper a method for optimal selection of analyzing window for generalized S-transform has been proposed. The S-transform is applied to the signal and the Smatrix is computed, firstly. Then, a concentration measure is introduced which can be calculated according to S-matrix. At last, the Particle Swarm Optimization (PSO) algorithm is employed to select an analyzing window which presents the best time-frequency resolution by minimizing the concentration measure. To evaluate the effectiveness of the proposed method, it has been implemented in Matlab environment and has been tested on the two most popular Gaussian and hyperbolic windows. Then, the improved S-transform has been applied to two sample signal cases and time-frequency contours have been plotted, accordingly. Also, the standard non-optimized Stransform has been implemented. The time-frequency resolutions of optimized Gaussian and hyperbolic windows have been compared to the time-frequency resolution presented by standard S-transform. It is found that both optimized hyperbolic as well as Gaussian widows provide a better time-frequency representation in comparison to non-optimal standard window.

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Energy concentration enhancement using window width optimization in S transform

Cited by 13 publications

References 8 publications

STFT With Adaptive Window Width Based on the Chirp Rate

STFT With Adaptive Window Width Based on the Chirp Rate

Low Computational Enhancement of STFT-Based Parameter Estimation

Optimal selection of analyzing window of arbitrary shape for S-transform using PSO algorithm

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