Averaged absolute spectral correlation density estimator

Shevgunov, Timofey; Efimov, Evgeniy; Zhukov, D. V.

doi:10.1109/mwent.2018.8337271

Cited by 14 publications

(2 citation statements)

References 6 publications

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“…Therefore, one may well rely on some criterion to distinguish it from the additive noise [ 48 ] or other components of the signal [ 49 ], which can produce a significant value at a local points due to the randomness of the estimate. The simple yet quite effective criterion can be based on the pseudo-power accumulated over the whole slice alongside frequency f axis, which was introduced in [ 50 ] and further applied in [ 51 ]:

where the summation by m is carried out within the support region of the SCF demarked in ( 13 ). If zero cyclic frequency is considered, i.e.,

, or

, this integral characteristic relates to the total power of the process, and power spectral density (PSD)

can be thought as the distribution of this power in the frequency f domain.…”

Section: Simulation Resultsmentioning

confidence: 99%

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

Shevgunov

Efimov

Guschina

2022

Sensors

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This article addresses the problem of estimating the spectral correlation function (SCF), which provides quantitative characterization in the frequency domain of wide-sense cyclostationary properties of random processes which are considered to be the theoretical models of observed time series or discrete-time signals. The theoretical framework behind the SCF estimation is briefly reviewed so that an important difference between the width of the resolution cell in bifrequency plane and the step between the centers of neighboring cells is highlighted. The outline of the proposed double-number fast Fourier transform algorithm (2N-FFT) is described in the paper as a sequence of steps directly leading to a digital signal processing technique. The 2N-FFT algorithm is derived from the time-smoothing approach to cyclic periodogram estimation where the spectral interpolation based on doubling the FFT base is employed. This guarantees that no cyclic frequency is left out of the coverage grid so that at least one resolution element intersects it. A numerical simulation involving two processes, a harmonic amplitude modulated by stationary noise and a binary-pulse amplitude-modulated train, demonstrated that their cyclic frequencies are estimated with a high accuracy, reaching the size of step between resolution cells. In addition, the SCF components estimated by the proposed algorithm are shown to be similar to the curves provided by the theoretical models of the observed processes. The comparison between the proposed algorithm and the well-known FFT accumulation method in terms of computational complexity and required memory size reveals the cases where the 2N-FFT algorithm offers a reasonable trade-off.

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where the summation by m is carried out within the support region of the SCF demarked in ( 13 ). If zero cyclic frequency is considered, i.e.,

, or

, this integral characteristic relates to the total power of the process, and power spectral density (PSD)

can be thought as the distribution of this power in the frequency f domain.…”

Section: Simulation Resultsmentioning

confidence: 99%

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

Shevgunov

Efimov

Guschina

2022

Sensors

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“…Since a high resolution in both distance (denoted by ρ) and angle (denoted by φ) for the observed radar target is assumed, the signal could be described with Equation 1where stands for complex-valued space-time radar signal echoed from p-th individual scatterer, is the slice of the radar image across the distance (in fact, the form of this slice will correspond to the form of probe pulse), is a term for squared antenna pattern, φ p determines the main beam direction of the antenna. The term n(t) describes the additive noise which exhibits stationary or cyclostationary properties (Shevgunov et al, 2018b;Shevgunov and Efimov, 2019b) in the frequency band width enough for efficient functioning of the radar system. The schematic structure of the reflected signal is shown in Figure 1, where one can see red thin solid line as the three-dimensional plot of the wave in the time domain.…”

Section: Methodsmentioning

confidence: 99%

Scattering Target Identification Based on Radial Basis Function Artificial Neural Networks in the Presence of Non-Stationary Noise

Shevgunov¹,

Efimov²,

Kirdyashkin³

2019

PTQ

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The paper deals with the radar target discrimination problem performed on complex radar images. The approach based on radial basis function (RBF) artificial neural network (ANN) is proposed for the identification of point scatterers placed within a radar image. The renewed concept of simple adaptive units as the foundation for network assembling allows one to design an ANN-based feature extraction scheme for the two-dimensional signal processing. It was shown that ANN implementing RBF neural processing units could be applied for the identification of radar targets described by the set of separated scatterers, even in cases where the relative distance between the scatterers is comparable to or less than the effective width of each scatterer. The obtained results indicate a high accuracy estimation of separate scatterer centers in the presence of noise which is not limited to the stationary case but supposed to be cyclostationary. It was also shown that the parameters describing the coordinates of scattering centers could be successfully extracted from the trained ANN after about one hundred epochs spent on ANN training process, which is carried out by means of modified gradient descent method. The main result is to demonstrate the possibility of using neural networks to automatically analyze radar images, which is an integral part of a set of tasks that form the target recognition problem. The proposed algorithm implements an approach of identification systems made using a neural network training procedures.

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Software Implementation of Spectral Correlation Density Analyzer with RTL2832U SDR and Qt Framework

Shevgunov

Efimov

2019

Advances in Intelligent Systems and Computing

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Averaged absolute spectral correlation density estimator

Cited by 14 publications

References 6 publications

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

Estimation of a Spectral Correlation Function Using a Time-Smoothing Cyclic Periodogram and FFT Interpolation—2N-FFT Algorithm

Scattering Target Identification Based on Radial Basis Function Artificial Neural Networks in the Presence of Non-Stationary Noise

Software Implementation of Spectral Correlation Density Analyzer with RTL2832U SDR and Qt Framework

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