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Journal articles on the topic 'Instantaneous frequency estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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1

Katkovnik, V. "Nonparametric estimation of instantaneous frequency." IEEE Transactions on Information Theory 43, no. 1 (1997): 183–89. http://dx.doi.org/10.1109/18.567676.

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2

Lam, Daniel, Brandon Buckley, Cejo Lonappan, Asad Madni, and Bahram Jalali. "Ultra-wideband instantaneous frequency estimation." IEEE Instrumentation & Measurement Magazine 18, no. 2 (April 2015): 26–30. http://dx.doi.org/10.1109/mim.2015.7066680.

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3

Zhao, Dezun, Len Gelman, Fulei Chu, and Andrew Ball. "Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions." Sensors 20, no. 18 (September 11, 2020): 5201. http://dx.doi.org/10.3390/s20185201.

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It is proposed a novel instantaneous frequency estimation technology, multi-generalized demodulation transform, for non-stationary signals, whose true time variations of instantaneous frequencies are unknown and difficult to extract from the time-frequency representation due to essentially noisy environment. Theoretical bases of the novel instantaneous frequency estimation technology are created. The main innovations are summarized as: (a) novel instantaneous frequency estimation technology, multi-generalized demodulation transform, is proposed, (b) novel instantaneous frequency estimation results, obtained by simulation, for four types of amplitude and frequency modulated non-stationary single and multicomponent signals under strong background noise (signal to noise ratio is −5 dB), and (c) novel experimental instantaneous frequency estimation results for defect frequency of rolling bearings for multiple defect frequency harmonics, using the proposed technology in non-stationary conditions and in conditions of different levels of noise interference, including a strong noise interference. Quantitative instantaneous frequency estimation errors are employed to evaluate performance of the proposed IF estimation technology. Simulation and experimental estimation results show high effectiveness of the proposed estimation technology.
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4

Riedel, K. S. "Kernel estimation of the instantaneous frequency." IEEE Transactions on Signal Processing 42, no. 10 (1994): 2644–49. http://dx.doi.org/10.1109/78.324730.

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5

Yang, Senlin, and Jinghuai Gao. "Seismic Attenuation Estimation From Instantaneous Frequency." IEEE Geoscience and Remote Sensing Letters 7, no. 1 (January 2010): 113–17. http://dx.doi.org/10.1109/lgrs.2009.2028302.

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6

Qiu, Lunji, Haiyun Yang, and Soo-Ngee Koh. "Fundamental frequency determination based on instantaneous frequency estimation." Signal Processing 44, no. 2 (June 1995): 233–41. http://dx.doi.org/10.1016/0165-1684(95)00027-b.

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7

Lu, Wen-kai, and Chang-Kai Zhang. "Robust estimation of instantaneous phase using a time-frequency adaptive filter." GEOPHYSICS 78, no. 1 (January 1, 2013): O1—O7. http://dx.doi.org/10.1190/geo2011-0435.1.

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The instantaneous phase estimated by the Hilbert transform (HT) is susceptible to noise; we propose a robust approach for the estimation of instantaneous phase in noisy situations. The main procedure of the proposed method is applying an adaptive filter in time-frequency domain and calculating the analytic signal. By supposing that one frequency component with higher amplitude has higher signal-to-noise ratio, a zero-phase adaptive filter, which is constructed by using the time-frequency amplitude spectrum, enhances the frequency components with higher amplitudes and suppresses those with lower amplitudes. The estimation of instantaneous frequency, which is defined as the derivative of instantaneous phase, is also improved by the proposed robust instantaneous phase estimation method. Synthetic and field data sets are used to demonstrate the performance of the proposed method for the estimation of instantaneous phase and frequency, compared by the HT and short-time-Fourier-transform methods.
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8

BO, Lin. "Improved method for Hilbert instantaneous frequency estimation." Chinese Journal of Mechanical Engineering (English Edition) 20, no. 06 (2007): 94. http://dx.doi.org/10.3901/cjme.2007.06.094.

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9

Mejia-Barron, Arturo, David Granados-Lieberman, Jose Razo-Hernandez, Juan Amezquita-Sanchez, and Martin Valtierra-Rodriguez. "Harmonic PMU Algorithm Based on Complex Filters and Instantaneous Single-Sideband Modulation." Electronics 8, no. 2 (January 29, 2019): 135. http://dx.doi.org/10.3390/electronics8020135.

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Phasor measurement units (PMUs) have become powerful monitoring tools for many applications in smart grids. In order to address the different issues related to harmonics in power systems, the fundamental phasor estimator in a PMU has been extended to the harmonic phasor estimator by several researchers around the world. Yet, the development of harmonic phasor estimators is a challenge because they have to consider time-varying frequencies since the frequency deviation in the harmonic components is proportional to the harmonic order in a dynamic way. In this work, a new algorithm for harmonic phasor estimation using an instantaneous single-sideband (SSB) modulation is presented. Unlike other SSB-based approaches, its implementation in this work is based on concepts of instantaneous phase and instantaneous frequency. In general, the proposed algorithm is divided into two stages. Firstly, the estimation of the fundamental phasor is carried out by means of a complex finite impulse response (FIR) filter which provides the analytic signal used to compute the instantaneous magnitude, phase, and frequency. Secondly, a complex FIR filter bank is proposed for the estimation of the harmonic components, where the instantaneous SSB modulation technique is applied in order to center the harmonic components into specific narrow bands for each complex filter when an off-nominal frequency occurs. The validation of the proposed algorithm is carried out by means of the current standards of phasor measurement units, i.e., Std. C37.118.1-2011 and C37.118.1a-2014, which involve steady-state, dynamic, and time performance tests.
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10

Wang, Xiao-Feng, Da-Wei Li, Hui-Xu Dong, and Run-Lan Tian. "Instantaneous Frequency Estimation of Nonlinear FM Radar Signal Based on Multi-Scale Chirplet Path." Journal of Nanoelectronics and Optoelectronics 17, no. 2 (February 1, 2022): 285–97. http://dx.doi.org/10.1166/jno.2022.3196.

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Instantaneous frequency is an important parameter to non-linear frequency modulated (NLFM) signal in low probability intercept (LPI) radar. For electronic intelligence, it is very important to accurately estimate instantaneous frequency of NLFM signal. A multi-scale chirplet path pursuit (MCPP) method and its improved method are proposed for electronic intelligence systems to estimate instantaneous frequency of NLFM radar signal in this paper. Firstly, signal duration is divided into a set of dynamic time interval, multi-scale chirplet basis function is established on each time interval simultaneously. And then, projection coefficient in each dynamic interval is calculated basing on chirplet basis functions. And then, chirplet basis functions which have the largest projection coefficient with the analysis signal in each time interval are connected by path pursuit algorithm. Rough estimation of instantaneous frequency will be achieved by connecting the linear frequency of those chirplet basis functions. At last, to solve the problem that instantaneous frequency curve is not smooth for the impact of noise and chirplet errors, least square fitting method is used to further improve estimation accuracy. Experimental results show that, proposed improved MCPP algorithm is suitable for the instantaneous frequency of the NLFM radar signal at low SNR. Compared with time-frequency analysis method, it has higher estimation accuracy. Proposed method can also be applied to the instantaneous frequencies estimation of other NLFM signal without prior knowledge, such as seismic signals and fault diagnosis signals.
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11

Yao, Rong Qing. "Research on an Algorithm and System for Estimation of Instantaneous Frequency of Rotating Machinery." Advanced Materials Research 452-453 (January 2012): 153–59. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.153.

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Instantaneous frequency is an import parameter to diagnose faults of rotating machinery. This paper puts forward an algorithm based Hilbert-Huang Transformation (HHT) to estimate the instantaneous frequency of rotating machinery and develops an instantaneous cymometer based embedded system technology. In order to estimate instantaneous frequency of rotating machinery, the vibration signal is decomposed into a series of intrinsic mode functions (IMF) first by the method of empirical mode decomposition (EMD), then one of the intrinsic mode functions is analyzed with the Hilbert transformation to acquire an estimate value of instantaneous frequency. An instantaneous cymometer is also described in this paper, which is designed to measure the average frequency and instantaneous frequency of rotating machinery in real time. The average frequency is acquired from measuring the cycle of key-phase signal, and the instantaneous frequency is from the above-mentioned method based HHT. The instantaneous cymometer is consisted of an embedded system, which is connected to a PC with an Ethernet. The embedded system is based on an ARM chip (Samsung S3C4510) A/D conversion, EMD and Hilbert transform are completed on the embedded system, and then the results are compressed and sent to the PC by TCP/IP.
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12

Emresoy, Mustafa K., and Amro El-Jaroudi. "Iterative instantaneous frequency estimation and adaptive matched spectrogram." Signal Processing 64, no. 2 (January 1998): 157–65. http://dx.doi.org/10.1016/s0165-1684(97)00183-7.

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13

Jiang, Qingtang, and Bruce W. Suter. "Instantaneous frequency estimation based on synchrosqueezing wavelet transform." Signal Processing 138 (September 2017): 167–81. http://dx.doi.org/10.1016/j.sigpro.2017.03.007.

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14

Öztürk, Mahmut, and Aydın Akan. "Local instantaneous frequency estimation of multi-component signals." Computers & Electrical Engineering 34, no. 4 (July 2008): 281–89. http://dx.doi.org/10.1016/j.compeleceng.2007.03.004.

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15

Li, Lingxiao, Zhixing Huang, and Wei Zhang. "INSTANTANEOUS FREQUENCY ESTIMATION METHODS OF MICRO-DOPPLER SIGNAL." Progress In Electromagnetics Research C 58 (2015): 125–34. http://dx.doi.org/10.2528/pierc15060203.

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16

Hush, Don, Nasir Ahmed, and Ruth David. "Instantaneous Frequency Estimation Using Adaptive Linear Predictor Weights." IEEE Transactions on Aerospace and Electronic Systems AES-22, no. 4 (July 1986): 422–31. http://dx.doi.org/10.1109/taes.1986.310778.

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17

O'Shea, P. "A new technique for instantaneous frequency rate estimation." IEEE Signal Processing Letters 9, no. 8 (August 2002): 251–52. http://dx.doi.org/10.1109/lsp.2002.803003.

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18

Stankovic, L., M. Dakovic, Jin Jiang, and E. Sejdic. "Instantaneous Frequency Estimation Using the ${\rm S}$-Transform." IEEE Signal Processing Letters 15 (2008): 309–12. http://dx.doi.org/10.1109/lsp.2008.917014.

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19

Spagnolini, U. "2-D phase unwrapping and instantaneous frequency estimation." IEEE Transactions on Geoscience and Remote Sensing 33, no. 3 (May 1995): 579–89. http://dx.doi.org/10.1109/36.387574.

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20

Chang, Yong ping, and Wei Dong Jiao. "Wholly ICA Based Estimation on DOA of Spatio-Temporal Sources." Advanced Materials Research 588-589 (November 2012): 739–46. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.739.

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A class of methods is presented for wholly estimating direction of arrival (DOA) of convolutively mixed sources in the frequency domain, which is based on independent component analysis (ICA). Convolutive mixtures of multiple sources in the spatio-temporal domain are firstly reduced to instantaneous mixtures by using the well-known short-time Fourier transformation (STFT) technique. From the time-frequency mixture in each frequency bin, one frequency respond matrix of the mixing system from sources to sensors is identified by some instantaneous ICA algorithms. Furthermore, DOAs of the multiple sources is estimated by using a whole estimating strategy. Consequently, all mixtures in total frequency bins contribute to a final estimation set, in which the source directions are shown as several direction clusters and/or local maxima. Experimental results indicate that the ICA based methods have some advantages over the well-known MUSIC (MUltiple SIgnal Classification) method not only on estimation precision of multiple source directions, but also on potential applicability under some especial conditions.
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21

Ostlund, N., J. Yu, and J. S. Karlsson. "Improved Maximum Frequency Estimation With Application to Instantaneous Mean Frequency Estimation of Surface Electromyography." IEEE Transactions on Biomedical Engineering 51, no. 9 (September 2004): 1541–46. http://dx.doi.org/10.1109/tbme.2004.827930.

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22

Hua, Zhai, Mao Hong-min, Wang Dong, Lu Xue-song, and Ding Xu. "A combined kernel-based fuzzy C-means clustering and spectral centroid for instantaneous frequency estimation." Advances in Mechanical Engineering 12, no. 9 (September 2020): 168781402091367. http://dx.doi.org/10.1177/1687814020913679.

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An improved instantaneous frequency estimation algorithm for rotating machines based on a kernel-based fuzzy C-means clustering (KFCM) algorithm used in association with a spectral centroid algorithm is proposed in this study. The clustering algorithm is used first to discriminate the time-frequency points from the sources of the reference axis and other points. The discrete time-frequency points related to the instantaneous rotation frequency of the reference axis are then located based on the values of the time-frequency matrix elements; on the basis of these elements, the instantaneous rotation frequency is then estimated using a spectral centroid algorithm. It is demonstrated that this method effectively reduces the effects of interference and noise while achieving higher estimation precision. To validate the proposed method, numerical simulations of multi-component signals and crossover signals are performed. The results of these simulations indicate that the method can realize instantaneous frequency estimation with high precision, even when the numerical responses are contaminated by Gaussian white noise. In addition, when this method is used to analyze the vibration signal of rotating machinery in the situation of a run-up procedure, remarkable speed estimation results are obtained.
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23

Huang, Yuexin, Qiukun Zhang, Jianfeng Zhong, Zhixiong Chen, and Shuncong Zhong. "Parameterized Instantaneous Frequency Estimation Method for Vibration Signal with Nonlinear Frequency Modulation." Machines 10, no. 9 (September 6, 2022): 777. http://dx.doi.org/10.3390/machines10090777.

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The vibration signal from the rotatory machinery condition monitoring under time-varying speed is usually amplitude-modulated (AM) and frequency-modulated (FM). It is important to efficiently and accurately estimate the instantaneous frequency (IF) of the vibration signal. In this paper, a novel parameterized IF estimation method is proposed. The method employs a high-order polynomial function to approximate the nonlinear IF and subsequently constructs overdetermined systems of linear equations by calculating the Fourier transform of the derivative of the signal. The IF can be estimated by using least squares estimation to solve the equations. The proposed method has high computational efficiency because it can obtain the estimation of IF over a period of time simultaneously; it differs from traditional time-frequency analysis methods that need to calculate the IF at each point in the time axis. It is demonstrated that the proposed method is not only particularly powerful for the nonlinear FM mono-component signal but also applicable to the multi-component signal constructed by multiple harmonics. The numerical simulation validates the effectiveness of the proposed method, and the experiment’s results show that the method is suitable for the IF estimation of the vibration signal from the varying-speed rotor system.
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24

Ma, Zengqiang, Wanying Ruan, Mingyi Chen, and Xiang Li. "An Improved Time-Frequency Analysis Method for Instantaneous Frequency Estimation of Rolling Bearing." Shock and Vibration 2018 (September 18, 2018): 1–18. http://dx.doi.org/10.1155/2018/8710190.

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Instantaneous frequency estimation of rolling bearing is a key step in order tracking without tachometers, and time-frequency analysis method is an effective solution. In this paper, a new method applying the variational mode decomposition (VMD) in association with the synchroextracting transform (SET), named VMD-SET, is proposed as an improved time-frequency analysis method for instantaneous frequency estimation of rolling bearing. The SET is a new time-frequency analysis method which belongs to a postprocessing procedure of the short-time Fourier transform (STFT) and has excellent performance in energy concentration. Considering nonstationary broadband fault vibration signals of rolling bearing under variable speed conditions, the time-frequency characteristics cannot be obtained accurately by SET alone. Thus, VMD-SET method is proposed. Firstly, the signal is decomposed into several intrinsic mode functions (IMFs) with different center frequency by VMD. Then, effective IMFs are selected by mutual information and kurtosis criteria and are reconstructed. Next, the SET method is applied to the reconstructed signal to generate the time-frequency representation with high resolution. Finally, instantaneous frequency trajectory can be accurately extracted by peak search from the time-frequency representation. The proposed method is free from time-varying sidebands and is robust to noise interference. It is proved by numerical simulated signal analysis and is further validated by lab experimental rolling bearing vibration signal analysis. The results show this method can estimate the instantaneous frequency with high precision without noise interference.
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25

LI, J., R. JIN, and J. GENG. "Estimation for Instantaneous Frequency Using a Combined Fractional Time-Frequency Representation." IEICE Transactions on Communications E90-B, no. 1 (January 1, 2007): 189–91. http://dx.doi.org/10.1093/ietcom/e90-b.1.189.

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26

Khan, Nabeel Ali, and Sadiq Ali. "Reconstruction of gapped missing samples based on instantaneous frequency and instantaneous amplitude estimation." Signal Processing 193 (April 2022): 108429. http://dx.doi.org/10.1016/j.sigpro.2021.108429.

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27

Sharma, Neeraj Kumar, and Thippur V. Sreenivas. "Event-triggered sampling using signal extrema for instantaneous amplitude and instantaneous frequency estimation." Signal Processing 116 (November 2015): 43–54. http://dx.doi.org/10.1016/j.sigpro.2015.03.025.

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28

Peng, Panpan, and Liang An. "Widely Linear Adaptive Instantaneous Frequency Estimation in Vector Hydrophones." Sensors 18, no. 10 (October 7, 2018): 3348. http://dx.doi.org/10.3390/s18103348.

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To solve the problem that the time-frequency resolution of Short-Time Fourier Transform (STFT) is constrained by the window length and the moving step of the short time window, and to utilize the merits of a widely linear method, a novel instantaneous frequency estimation method in vector hydrophone was proposed. In this paper, a complex variable was constructed. It is composed of sound pressure and particle velocity as its real part and imaginary part, respectively. The constructed variable was approved to be second order noncircular (improper). For the modelling of noncircular signals, the standard linear estimation is not adequate and the pseudo-covariance matrix should also be taken into consideration. As a result, a widely linear adaptive instantaneous frequency estimation algorithm and its three solutions based on the augmented complex least mean square (ACLMS) method are presented to estimate the instantaneous frequency in vector hydrophones. The results of simulations and laboratory experiments prove that this approach based on a widely linear model performs better compared to STFT and strict linear filter methods.
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29

Milczarek, Hubert, Czesław Leśnik, Igor Djurović, and Adam Kawalec. "Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study." Sensors 21, no. 8 (April 17, 2021): 2840. http://dx.doi.org/10.3390/s21082840.

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Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.
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30

Ma, Yanxin, Yifan Zhang, Jiahua Zhu, Ke Xu, and Yujin Cai. "A Fast Instantaneous Frequency Estimation for Underwater Acoustic Target Feature Extraction." Journal of Physics: Conference Series 2031, no. 1 (September 1, 2021): 012018. http://dx.doi.org/10.1088/1742-6596/2031/1/012018.

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Abstract Traditional auditory features merely present the amplitude characteristics of target signals in frequency domain. Such features are susceptible to environmental noise, resulting in significant degradation of recognition stability. Inspired by instantaneous information applied in speech signal processing field, this paper proposed a feature extraction method using sub-based instantaneous frequency. A fast instantaneous frequency information extraction algorithm is proposed with the normalized Gammatone filterbanks. Experiments confirm that the proposed feature extraction method can effectively maintain the recognition accuracy under low SNR conditions while reduce the computation cost.
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31

Bonnardot, Frédéric, Khalid Lizoul, Saad Errafik, Hugo André, and François Guillet. "High frequency demodulation technique for instantaneous angular speed estimation." Mechanical Systems and Signal Processing 159 (October 2021): 107745. http://dx.doi.org/10.1016/j.ymssp.2021.107745.

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32

Liu, Qidi, Benjamin Gily, and Mable P. Fok. "Adaptive Photonic Microwave Instantaneous Frequency Estimation Using Machine Learning." IEEE Photonics Technology Letters 33, no. 24 (December 15, 2021): 1511–14. http://dx.doi.org/10.1109/lpt.2021.3128867.

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33

Samaha, Jason, and Michael X. Cohen. "Power spectrum slope confounds estimation of instantaneous oscillatory frequency." NeuroImage 250 (April 2022): 118929. http://dx.doi.org/10.1016/j.neuroimage.2022.118929.

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34

Peng, Z. K., G. Meng, F. L. Chu, Z. Q. Lang, W. M. Zhang, and Y. Yang. "Polynomial Chirplet Transform With Application to Instantaneous Frequency Estimation." IEEE Transactions on Instrumentation and Measurement 60, no. 9 (September 2011): 3222–29. http://dx.doi.org/10.1109/tim.2011.2124770.

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35

Terzija, Vladimir, Deyu Cai, Vladimir Stanojevic, and Goran Strbac. "Frequency and Power Components Estimation from Instantaneous Power Signal." IEEE Transactions on Instrumentation and Measurement 60, no. 11 (November 2011): 3640–49. http://dx.doi.org/10.1109/tim.2011.2138190.

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36

Rao, P., and F. J. Taylor. "Estimation of instantaneous frequency using the discrete Wigner distribution." Electronics Letters 26, no. 4 (1990): 246. http://dx.doi.org/10.1049/el:19900165.

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37

Girolami, Gérard, and David Vakman. "Instantaneous frequency estimation and measurement: a quasi-local method." Measurement Science and Technology 13, no. 6 (May 22, 2002): 909–17. http://dx.doi.org/10.1088/0957-0233/13/6/312.

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38

Sekhar, S. C., and T. V. Sreenivas. "Auditory motivated level-crossing approach to instantaneous frequency estimation." IEEE Transactions on Signal Processing 53, no. 4 (April 2005): 1450–62. http://dx.doi.org/10.1109/tsp.2005.843736.

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39

Djurović, Igor. "Viterbi algorithm for chirp-rate and instantaneous frequency estimation." Signal Processing 91, no. 5 (May 2011): 1308–14. http://dx.doi.org/10.1016/j.sigpro.2010.10.007.

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40

Lin, Wang, and Meng Xiaofeng. "An adaptive Generalized S-transform for instantaneous frequency estimation." Signal Processing 91, no. 8 (August 2011): 1876–86. http://dx.doi.org/10.1016/j.sigpro.2011.02.010.

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41

Xu, Xiao-Na, Bing-Zhao Li, and Xiu-Ling Ma. "Instantaneous frequency estimation based on the linear canonical transform." Journal of the Franklin Institute 349, no. 10 (December 2012): 3185–93. http://dx.doi.org/10.1016/j.jfranklin.2012.09.014.

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42

Youn, D. H., V. J. Mathews, and S. H. Cho. "An efficient lattice predictor algorithm for instantaneous frequency estimation." Signal Processing 10, no. 1 (January 1986): 75–81. http://dx.doi.org/10.1016/0165-1684(86)90066-6.

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43

Yegnanarayana, B., and K. S. R. Murty. "Event-Based Instantaneous Fundamental Frequency Estimation From Speech Signals." IEEE Transactions on Audio, Speech, and Language Processing 17, no. 4 (May 2009): 614–24. http://dx.doi.org/10.1109/tasl.2008.2012194.

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44

Chui, Charles K., and Maria D. van der Walt. "Signal analysis via instantaneous frequency estimation of signal components." GEM - International Journal on Geomathematics 6, no. 1 (February 25, 2015): 1–42. http://dx.doi.org/10.1007/s13137-015-0070-z.

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45

Yan, Lu, Chen Qin-Xiao, Zhong Cheng, Tao Xian, Ding En-Jie, Liu Ke, Du Juan, and Tong Xiao-Chun. "Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation." Advances in Mechanical Engineering 15, no. 5 (May 2023): 168781322311724. http://dx.doi.org/10.1177/16878132231172489.

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Mechanical equipment often works on variable speed condition, its corresponding vibration signal presents multi-component, modulation coupling with fast time-varying instantaneous frequency (IF), how to effectively compute IF and realize fasting varying non-stationary signal decoupling separation plays an important role in mechanical system fault diagnosis. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to identify, extract, and trend IF for achieving a highly concentrated time-frequency energy. Simulation demonstrates that the proposed method performs better than traditional IF estimation method. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 10%.
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46

Cai, Haiyan, Qingtang Jiang, Lin Li, and Bruce W. Suter. "Analysis of adaptive short-time Fourier transform-based synchrosqueezing transform." Analysis and Applications 19, no. 01 (May 23, 2020): 71–105. http://dx.doi.org/10.1142/s0219530520400047.

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Recently, the study of modeling a non-stationary signal as a superposition of amplitude and frequency-modulated Fourier-like oscillatory modes has been a very active research area. The synchrosqueezing transform (SST) is a powerful method for instantaneous frequency estimation and component separation of non-stationary multicomponent signals. The short-time Fourier transform-based SST (FSST) reassigns the frequency variable to sharpen the time-frequency representation and to separate the components of a multicomponent non-stationary signal. Very recently the FSST with a time-varying parameter, called the adaptive FSST, was introduced. The simulation experiments show that the adaptive FSST is very promising in instantaneous frequency estimation of the component of a multicomponent signal, and in accurate component recovery. However, the theoretical analysis of the adaptive FSST has not been carried out. In this paper, we study the theoretical analysis of the adaptive FSST and obtain the error bounds for the instantaneous frequency estimation and component recovery with the adaptive FSST and the second-order adaptive FSST.
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47

Djurović, Igor. "Combination of the viterbi algorithm and cross-wigner distribution for the instantaneous frequency estimation phase signals in high noise environments." Journal of Electrical Engineering 69, no. 3 (June 1, 2018): 255–58. http://dx.doi.org/10.2478/jee-2018-0035.

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Abstract In this paper, combination of the cross-Wigner distribution (XWD) and the Viterbi algorithm (VA) for the instantaneous frequency (IF) estimation of frequency modulated (FM) signals in high noise environments is proposed. The favourable properties of the VA, the IF reconstruction based on minimization of the path penalty functions, and the XWD, iterative accuracy improvement of the IF estimation, give hybrid IF estimator with improved accuracy for high noise environments
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48

Zhou, Yuan, Bin Chen, Bao Cheng Gao, and Si Jie Zhang. "Variable Speed Estimation for Wheel-Bearings with STFT and BP Neural Network." Advanced Materials Research 945-949 (June 2014): 1112–15. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.1112.

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For the variable speed estimation of wheel-bearings in strong background noise, a novel method with the short-time Fourier transform and BP neural network (STFT-BPNN) is proposed. In the method, it calculates the time-frequency spectrum with STFT technique. Then the instantaneous frequency is estimated by peak detection. Taking the instantaneous frequencies as the input vectors, the BP neural network is trained to fit the discrete instantaneous frequencies. The effectiveness of proposed method is demonstrated by simulation. Experimental results show that proposed method provides better performance on variable speed estimation for wheel-bearings.
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49

Qian, Bo, Xiao Yu Zhang, and Shuang Sun. "A Parameters Estimation Method for FH Signal Based on SPWVD." Advanced Materials Research 912-914 (April 2014): 1112–15. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.1112.

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Based on analyzing the generation mechanism and characteristics of frequency hopping signal, a method for estimating the parameter of FH signal utilizing SPWVD is proposed. The hop dwell time is estimated by detected frequency transforming moments between conjoint hop in time and frequency array. Further, the hop rate, instantaneous frequency is estimated. The results of simulation show that the method can effectively estimate the parameter of FH signal, has better accuracy of parameter estimation and less amount of computation.
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50

Abdoush, Yazan, Jose A. Garcia-Molina, and Giovanni E. Corazza. "Adaptive instantaneous frequency estimation based on time-frequency distributions with derivative approximation." Signal Processing 160 (July 2019): 99–105. http://dx.doi.org/10.1016/j.sigpro.2019.01.027.

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