ℓp-STFT: A Robust Parameter Estimator of a Frequency Hopping Signal for Impulsive Noise

Su, Yang; Wang, Lina; Chen, Yuan; Yang, Xiaolong

doi:10.3390/electronics8091017

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…Because (10) and (11) have similar forms, we only consider (10) and minimize it. To minimize (10), we set Figure 16a shows the correct rates of the CSML algorithm and the CSOMP-ASW algorithm for frequency estimation of multiple FH signals with frequency-switching time.…”

Section: Appendix Amentioning

confidence: 99%

“…In order to intercept and jam the frequency-hopping signals of non-cooperative targets, precise knowledge of the parameters of their transmitted FH signals is required [9]. Therefore, parameter estimation of frequency-hopping (FH) signals is an important task for information interception and autonomous electronic countermeasures [10,11]. The battlefield environment is rapidly changing, and the fast estimation of FH signals in the case of unknown frequency, an unknown number of signals or signals with frequency-switching time is of great importance.…”

Section: Introductionmentioning

confidence: 99%

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A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

Wang

Fan

et al. 2023

Electronics

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The parameter estimation of multiple frequency-hopping (multiple FH) signals with frequency-switching time is a great challenge under conditions in which the number of signals is unknown. Due to the increasing mobility of devices such as unmanned aerial vehicles (UAVs), speed of parameter estimation is even more demanding. To solve this problem, an algorithm for estimating parameters of multiple FH signals based on compressed spectrum sensing and maximum likelihood (CSML) theory is proposed in this paper. First, the received signal is split into segments of the same length, and the frequencies contained in each segment are extracted using compressed spectrum sensing and kurtosis threshold. Next, the frequencies contained in adjacent segments are compared to find the signal segment in which frequency hopping occurs and its corresponding frequency. Finally, a hopping-time fast estimation algorithm based on the maximum likelihood theory is used to estimate the hopping time. Simulation results show that the algorithm proposed in this paper can estimate the parameters of multiple FH signals quickly and accurately when the number of signals is unknown and that it is equally effective for multiple FH signals with frequency-switching time.

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Section: Appendix Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

Wang

Fan

et al. 2023

Electronics

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show abstract

“…Aiming at the fractional low-order time-frequency distribution methods in α stable distribution environment, linear fractional low order short time Fourier transform (FLOSTFT) time-frequency representation method has no cross term interference, but its resolution is not high [27]. The resolution of the linear fractional low order Stockwell transform (FLOST) timefrequency representation method is improved to some extent [28].…”

Section: Introductionmentioning

confidence: 99%

Fractional lower order linear chirplet transform and its application to bearing fault analysis

et al. 2022

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The amplitude and frequency of the mechanical bearing fault vibration signals vary with time, and which are non-stationary and non-Gaussian process. The fault signals belong to α stable distribution, and the characteristic index 1 < α < 2, even the noises are α stable distribution in extreme cases. The existing linear chirplet transform (LCT) degenerates, even fails under α stable distribution environment. A fractional low order linear chirplet transform (FLOLCT) which takes advantage of fractional p order moment is presented for α stable distribution noise environment, and the corresponding FLOLCT time-frequency representation (FLOLCTTFR) is developed in this paper. By employing a series of polynomial chirp rate parameters instead of a single chirp rate of the FLOLCT method, a fractional low order polynomial linear chirplet transform (FLOPLCT) is developed to improve time frequency concentration of the signals. The improved FLOLCT and FLOPLCT methods are used to compare with the existing LCT and PLCT methods based on second order statistics, the results reveal performance advantages of the proposed methods. Finally, the FLOLCT and FLOPLCT methods are applied to analyze the fault signature of the bearing ball fault data in the position of DE (Drive end accelerometer) and extract their fault signature, the result illustrates their performances.

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Spectrum sensing for cognitive radio based on Kendall's tau in the presence of non-Gaussian impulsive noise

Lai

Dai

et al. 2022

Digital Signal Processing

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ℓp-STFT: A Robust Parameter Estimator of a Frequency Hopping Signal for Impulsive Noise

Cited by 6 publications

References 25 publications

A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

A Fast Estimation Algorithm for Parameters of Multiple Frequency-Hopping Signals Based on Compressed Spectrum Sensing and Maximum Likelihood

Fractional lower order linear chirplet transform and its application to bearing fault analysis

Spectrum sensing for cognitive radio based on Kendall's tau in the presence of non-Gaussian impulsive noise

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