Methodological Approach to Reducing the Radar Cross Section of Local Scatterer under Given Frequency-Angular Observation Conditions

This paper focuses on application of spectral estimation methods for scattering center’s radar cross section estimation and reduction under given frequency-angular observation conditions. A methodological approach has been developed to reduce the local center’s radar cross section with given object overall dimensions. The developed methodological approach is based on parametric optimization of object geometry, firstly, to reduce the local scatterer radar cross section and, secondly, to maximize object payload. The problem overview is presented in the introduction. The first section is devoted to mathematical formulation of the problem. The following section includes the comparison analysis of the different types of geometrical shapes. As a result, the object with exponential profile is chosen as the best one due to the ability to manage rear vertex local scatterer amplitude by changing the curvature parameter. In the third section the optimal curvature parameter value of the exponential profile is justified for the given object overall dimensions and frequency-angular observation conditions. It is demonstrated that the main characteristic to analysis is two-dimension functional dependence of the local scatterer mean radar cross section from geometrical parameter and angle of observation. It is proved that this mentioned dependence may be received by the implementation such well-known spectral estimation method as CLEAN to the object sinogram. The recognition range is calculated for two different hypothetical radars to assert the efficiency. It is offered in the conclusion to complicate the developed approach with radio absorption materials implementation as the direction of the future investigations.

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Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

10.1101/2021.06.22.449393 ◽

2021 ◽

Author(s):

Joachim Gross ◽

Daniel S. Kluger ◽

Omid Abbasi ◽

Nikolas Chalas ◽

Nadine Steingraeber ◽

...

Keyword(s):

Frequency Domain ◽

Phase Distribution ◽

Spectral Estimation ◽

Sampling Rate ◽

Group Analysis ◽

Estimation Method ◽

Gaussian Copula ◽

Estimation Methods ◽

Single Subject ◽

Connectivity Measure

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, wppc and gcmi tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appeared to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

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Insect Mass Estimation Based on Radar Cross Section Parameters and Support Vector Regression Algorithm

Remote Sensing ◽

10.3390/rs12111903 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1903

Author(s):

Cheng Hu ◽

Shaoyang Kong ◽

Rui Wang ◽

Fan Zhang ◽

Lianjun Wang

Keyword(s):

Support Vector Regression ◽

Cross Section ◽

Radar Cross Section ◽

Estimation Methods ◽

Estimation Accuracy ◽

Support Vector ◽

Feature Subset ◽

Mass Estimation ◽

Optimal Feature Subset ◽

Optimal Feature

Radar cross section (RCS) parameters of insect targets contain information related to their morphological parameters, which are helpful for the identification of migratory insects. Several morphological parameter estimation methods have been presented. However, most of these estimations are performed based on polynomial fitting methods, using only one or two parameters, which may limit the estimation accuracy. In this paper, a new insect mass estimation method is proposed based on support vector regression (SVR). Several RCS parameters were extracted for the estimation of insect mass. Support vector regression based on recursive feature elimination (SVRRFE) was used to obtain the optimal feature subset. Specifically, a dataset including 367 specimens was included to evaluate the performance of the proposed method. Fifteen features were extracted and ranked. The optimal feature subset contained six features and the optimal mass estimation accuracy was 78%. Additionally, traditional insect mass estimation methods were analyzed for comparison. The results prove that the proposed method is more effective and accurate for insect mass estimation. It needs to be emphasized that the poor number of experimental insects available may limit the further improvement of estimation accuracy.

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Radar cross-section data encoding based on AR spectral estimation techniques

Proceedings International Radar Conference ◽

10.1109/radar.1995.522541 ◽

2002 ◽

Author(s):

M.M. Williams ◽

A.A. Beex

Keyword(s):

Cross Section ◽

Spectral Estimation ◽

Radar Cross Section ◽

Cross Section Data ◽

Estimation Techniques ◽

Section Data ◽

Data Encoding

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Hybrid Deflection of Spoiler Influencing Radar Cross-Section of Tailless Fighter

Sensors ◽

10.3390/s21248459 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8459

Author(s):

Zeyang Zhou ◽

Jun Huang

Keyword(s):

Cross Section ◽

Electromagnetic Scattering ◽

Deflection Angle ◽

Elevation Angle ◽

Radar Cross Section ◽

Control Surface ◽

Linear Mode ◽

Linear Dynamic ◽

The Given ◽

Control Surfaces

With the continuous development of advanced fighters towards tailless and flying wing layouts, diverse control surfaces have become the mainstream design. To study the influence of spoiler control surface on the radar cross-section (RCS) of a tailless fighter, a calculation method is presented. The deflection angle of the spoiler is controlled by the fixed mode, linear mode, and smooth mode. The results show that the opening action of the spoiler will break the original stealth characteristics of the aircraft at the key azimuth angles of the head and tail. As the elevation angle increases, this adverse effect will spread to the side. The influence of the different dynamic deflection modes of the spoiler on the aircraft RCS is analyzed. Compared with the linear dynamic deflection mode, the smooth dynamic deflection mode is conducive to the reduction in the average RCS at the given head azimuth. The presented method is effective to study the influence of the spoiler deflection on the electromagnetic scattering characteristics of the tailless aircraft.

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Nonparametric and parametric methods of spectral analysis

MATEC Web of Conferences ◽

10.1051/matecconf/201928307002 ◽

2019 ◽

Vol 283 ◽

pp. 07002 ◽

Cited By ~ 1

Author(s):

Hangfang Zhao ◽

Lin Gui

Keyword(s):

Time Series ◽

Spectral Analysis ◽

High Resolution ◽

Spectral Estimation ◽

Estimation Method ◽

Parametric Estimation ◽

Stationary Time Series ◽

Estimation Methods ◽

Practical Applications ◽

Small Variance

Spectral Analysis is one of the most important methods in signal processing. In practical application, it is critical to discuss the power spectral density estimation of finite data sampled from some stationary time series. A spectral estimator is expected to have good statistical properties such as consistency, high resolution and small variance. For one spectral estimation method, there exists a trade-off between high resolution and small variance. The paper provides a comparison of several popular spectral methods from both theoretical properties and practical applications. We first address several basic nonparametric methods, whose statistical characters are analysed. Then we explain the connections and differences between temporal windowing and lag windowing. Thereafter, the confidence intervals of both windows are given and used to evaluate the estimated results. Besides, several different parametric estimation methods of autoregressive time series are compared, and whose properties and effects are also introduced. Building on our understanding of these studies, we then apply parametric and nonparametric spectral estimation methods on the data of ocean surface wave height.

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