Cyclostationarity: Limits and generalizations

This study deals with investigating the chirp signal detection obtained from the moving target in the presence of the destructive factor (leakage) and Gaussian noise using the generalised almost-cyclostationary (GACS) and Hough transform (HT) signal processing altogether (i.e. GACS-HT) approaches. To do so, a receiver was designed hierarchically using the above approach in this study. In this receiver, the detection and extraction of linear frequency modulation takes place in the subspace of the cyclostationary and Hough which have unique characteristics. In order to detect the target, the relative velocity was analysed in the cyclostationary subspace while the relative acceleration was analysed in the Hough space. Due to using the simulation, the behaviour of this receiver was measured by different subspaces for different parameters in the presence of noise and destructive factor. Ultimately, the efficiency of the presented approach was analysed and compared with other approaches. The results indicated that the GACS-HT approach is more efficient than the energy detector approach and in terms of complexity it is almost as complex as the LV transform approach.

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“…D T (t) and D L (t) are delays from target and leakage path, respectively. The delay is defined in radar as [30]…”

Section: Gacs Methodsmentioning

confidence: 99%

Detection of the chirp signal features caused by Doppler phenomenon in the presence of destructive agents based on cyclostationary and Hough transform methods

Heydari

Azmi

Arand

et al. 2018

IET signal process.

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“…x ðτÞ ¼ huðt þ τ=2Þv Ã ðt−τ =2Þi where u(t) = x(t)e −jπεt and v(t) = x(t)e jπεt . By using the properties of z 〈k〉 e ±jπεt = (ze ±jπεt ) 〈k〉 and (z * ) 〈k〉 = (z 〈k〉 ) * , the pth-order cyclic correlation function (19) can be rewritten as…”

Section: The Proposed Smoothing Fractional Lower-order Cyclic Doa Metmentioning

confidence: 99%

“…If received signals can be divided into the signal of interest (SOI) and interference (including noise) based on their cyclostationary characteristics, the signals of interest can be selected and the noise and interfering signals can be removed by utilizing cyclostationarity [17]. Therefore, there are many advantages of DOA estimation using cyclostationary characteristics compared to conventional methods [18,19]. These advantages include selective direction finding, interference and noise suppression, and breakthrough-limited multi-signal processing.…”

Section: Introductionmentioning

confidence: 99%

Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

Liu

Zhang

et al. 2019

J Wireless Com Network

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Estimating direction of arrival (DOA) is important in a variety of practical applications. Conventional cyclostationarity-based coherent DOA estimation algorithms are not robust to non-Gaussian α-stable impulsive noise. Additionally, fractional lower-order statistics (FLOS)-based algorithms are tolerant to impulsive noise; however, they experience performance degradation for coherent signals and interference. To overcome these drawbacks, two types of fractional lower-order cyclostationarity-based subspace DOA estimation methods are proposed for coherent signals in the presence of interference and α-stable impulsive noise. The new proposed algorithms exploit the fractional lower-order cyclostationarity properties of the signals and are immune to the impulsive noise and interference. Moreover, they can provide more accurate DOA estimates of coherent signals than conventional cyclostationarity-based and FLOS-based methods. The simulation results illustrate the robustness and effectiveness of the proposed methods for coherent signals based on a comparison with traditional methods. The new algorithms can be used in the presence of a wide range of interference, Gaussian noise, and α-stable distribution impulsive noise environments.

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“…Since then, other applications of this theory have emerged in numerous and varied fields such as rotating machinery [7], econometrics [8] or even radioastronomy [9]. Reviews on cyclostationarity can be found in [10], [11], [12].…”

Section: A the Property Of Cyclostationaritymentioning

confidence: 99%

Cyclostationary analysis of ECG signals acquired inside an ultra-high field MRI scanner

Haritopoulos

Krug

Illanes

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper, a strategy is proposed to estimate the R-peaks in ECG signals recorded inside a 7 T magnetic resonance imaging (MRI) scanner in order to reduce the disturbances due to the magnetohydrodynamic (MHD) effect and to finally obtain high quality cardiovascular magnetic resonance (CMR) images. We first show that the cyclostationarity property of the ECG signal disturbed by the MHD effect can be quantified by means of cyclic spectral analysis. Then, this information is forwarded as input to a cyclostationary source extraction algorithm applied to a set of ECG recordings acquired inside the MRI scanner in a Feet first (Ff) and a Head first (Hf) positions. Finally, detection of the R-peaks in the estimated cyclostationary signal completes the proposed procedure. Validation of the method is performed by comparing the estimated with clinical R-peaks annotations provided with the real world dataset. The obtained results are promising and future research directions are discussed.

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Cyclostationarity: Limits and generalizations

Cited by 69 publications

References 52 publications

Detection of the chirp signal features caused by Doppler phenomenon in the presence of destructive agents based on cyclostationary and Hough transform methods

Detection of the chirp signal features caused by Doppler phenomenon in the presence of destructive agents based on cyclostationary and Hough transform methods

Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

Cyclostationary analysis of ECG signals acquired inside an ultra-high field MRI scanner

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