Senem Makal Yucedag

Radar cross section calculation of a wind turbine modeled by PEC canonical structures

Wind turbines are electrically large and complex objects that act as scatterer for electromagnetic waves. Thus, they may cause performance degradation for radar systems. Radar simulations are useful tools to obtain suitable wind turbine sites around radar bases. Radar cross section (RCS) calculation is an important step in radar simulations. In this work, RCS of a wind turbine which is modeled by canonical structures is calculated analytically and validated with Physical Optics (PO) method.

Wind farms' interference effects on the error performance of wireless line-of-sight communications using binary digital modulations

The main objective of this paper is to examine the deterioration on the average error performance of military and civilian wireless communications systems (WCSs) that are in the vicinity of wind farms. By treating the total interference signal caused by any wind turbine (WT) as the ensemble of scattering signal replicas over the uniformly segmented parts of the WT that face different Doppler frequency shifts, path loss factors, etc., the time-varying characteristics of the total signal have been investigated by means of the varying configuration parameters. To expose the degrading effects of the wind farms on the received signal and the overall error performance of the WCSs, the scattering signal contribution of the segments of the mast and blades have been evaluated analytically by also taking physical optics-based radar cross-section (RCS) estimation method into account. Average bit error rate performances of digital modulation schemes have been examined theoretically and numerically via Monte Carlo simulations through the entire rotation period of the blades. With the aim of providing useful insights on the assessment of the performance degradation caused by the WT interference, this paper exposes miscellaneous numerical results related to different configurations and settlings.

Investigation of deteriorating effects of wind farms on SAR imaging

In this work, a Synthetic Aperture Radar (SAR) signal model that considers the rotational movement and scattering of wind turbines is proposed. The Radar Cross Sections (RCS) of the wind turbines that are modelled via the assembly of canonical structures are included in the signal model and the deteriorating effects of wind farms on SAR images are investigated. These SAR images are compared to the SAR images that are obtained without using RCS values and the significance of considering the time-varying scattering characteristics of wind turbines on SAR imaging is put forth.

CLEAN based wind turbine clutter mitigation approach for pulse-Doppler radars

In this work, a novel wind turbine clutter (WTC) mitigation approach for pulse-Doppler radars (PDRs) is proposed. The proposed mitigation approach consists of two signal processing stages. At the first stage, locations of the wind turbines (WTs) are detected through thresholding the average adaptive clutter maps which are generated by using the data of several radar scans. The second stage deals with the subtraction of WT signal contributions for the detected WT locations from matched-filtered radar range profiles via employing the CLEAN algorithm. After removing the WT signal contributions, pulse-Doppler detection processing could result in WTC-free detections. The effectiveness of the proposed method is demonstrated through simulation results.

Investigation of TMz scattering from conducting target buried in two-layered lossy medium with flat surfaces using a decomposition solution

A solution to electromagnetic scattering from a cylindrical object of arbitrary cross-section buried in a lossy-grounded dielectric medium with flat interfaces based on surface equivalence principle and a decomposition technique is developed. A set of electric field integral equations (EFIEs) for the currents on the object and the portion of the surfaces most strongly interacting with the object are obtained, and then the method of moment is used to solve these EFIEs in the frequency domain to obtain the scattered electric field.

The effect of the surface roughness to the classification of buried cylindrical targets

The electric field scattered from the buried cylindrical target is calculated by using Equivalence Principle and Moment Method, and the database is formed by using the scattered fields from three cylindrical targets at certain frequencies. Radial Basis Function Network is proposed for target classification. A portion of the database is used to train the network and the rest is used to test the performance of the neural network. This target classification process has been repeated by changing the surface roughness of the medium, in which is the target buried, and effects of the surface roughness to the target classification are investigated.

An artificial neural network application for buried target classification

Design of an electromagnetic target classifier is demonstrated in this study. The surface equivalence principle and the method of moment are used to calculate the electric field scattered from buried cylindrical target. A database is obtained by the scattered fields from two cylindrical targets at certain frequencies. While a portion of the database is used to train the network, the rest is used to test the performance of the neural network. Radial basis function network is proposed for target classification. This target classification process is repeated by changing the surface roughness and electrical parameters of the medium in which the target is buried, incident angle of the incident wave and cross section of the cylindrical target. Effects of these parameters to the target classification are investigated.Design of an electromagnetic target classifier is demonstrated in this study. The surface equivalence principle and the method of moment are used to calculate the electric field scattered from buried cylindrical target. A database is obtained by the scattered fields from two cylindrical targets at certain frequencies. While a portion of the database is used to train the network, the rest is used to test the performance of the neural network. Radial basis function network is proposed for target classification. This target classification process is repeated by changing the surface roughness and electrical parameters of the medium in which the target is buried, incident angle of the incident wave and cross section of the cylindrical target. Effects of these parameters to the target classification are investigated.

Cross section optimization of offset parabolic reflector antenna for cosecant-squared radiation pattern

This paper presents cross section optimization of offset parabolic reflector antenna structure to obtain cosecant-squared pattern for microwave surveillance radars. Analytical Regularization Method (ARM) and Artificial Neural Network (ANN) are used as a pre-design and optimization tool.