Compressive Frequency Hopping Signal Detection Using Spectral Kurtosis and Residual Signals

Hu, Chenlin; Kim, Jin Young; Na, Seung You; Kim, Hyoung‐Gook; Choi, Seung-Ho

doi:10.1007/s11277-015-3156-x

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…The kurtosis value ( ε ), defined by ( 17) [35], is sensitive to the presence of peaks in the data; the spectrum in Figure 3a contains three peaks, and the kurtosis value ( ε ) of this spectrum is equal to 19.7, while the spectrum in Figure 3b does not contain peaks, and the kurtosis value ( ε ) of this spectrum is 1.6.…”

Section: The Frequency Estimation Algorithmmentioning

confidence: 99%

“…Due to the presence of noise, the spectrum contains a large number of non-zero values. It is risky to discriminate whether the frequency is the frequency of multiple FH signals by setting a threshold on the value of p.The kurtosis value (ε), defined by (17)[35], is sensitive to the presence of peaks in the data; the spectrum in Figure3acontains three peaks, and the kurtosis value (ε) of this spectrum is equal to 19.7, while the spectrum in Figure3bdoes not contain peaks, and the kurtosis value (ε) of this spectrum is 1.6.…”

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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: The Frequency Estimation Algorithmmentioning

confidence: 99%

mentioning

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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“…However, the process of signal reconstruction is difficult and involves complex iterative algorithms that increase the amount of data. Therefore, a new signal processing method based on compressive detection (CD) has attracted increasing attention [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A two-stage classification algorithm for radar targets based on compressive detection

Liu

Zhang

et al. 2021

EURASIP J. Adv. Signal Process.

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Algorithms are proposed to address the radar target detection problem of compressed sensing (CS) under the conditions of a low signal-to-noise ratio (SNR) and a low signal-to-clutter ratio (SCR) echo signal. The algorithms include a two-stage classification for radar targets based on compressive detection (CD) without signal reconstruction and a support vector data description (SVDD) one-class classifier. First, we present the sparsity of the echo signal in the distance dimension to design a measurement matrix for CD of the echo signal. Constant false alarm rate (CFAR) detection is performed directly on the CD echo signal to complete the first-order target classification. In simulations, the detection performance is similar to that of the traditional matched filtering algorithm, but the data rate is lower, and the necessary data storage space is reduced. Then, the power spectrum features are extracted from the data after the first-order classification and converted to the feature domain. The SVDD one-class classifier is introduced to train and classify the characteristic signals to complete the separation of the targets and the false alarms. Finally, the performance of the algorithm is verified by simulation. The number of false alarms is reduced, and the detection probability of the targets is improved.

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On the Technology of Frequency Hopping Communication Network-Station Selection

Man

Hong

et al. 2021

2021 International Conference on Electronics, Circuits and Information Engineering (ECIE)

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Compressive Frequency Hopping Signal Detection Using Spectral Kurtosis and Residual Signals

Cited by 3 publications

References 15 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

A two-stage classification algorithm for radar targets based on compressive detection

On the Technology of Frequency Hopping Communication Network-Station Selection

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