Osman Karabayir

Performance analysis of compressive ISAR imaging for complex targets

In this paper, performance efficiency analysis of compressive inverse synthetic aperture radar (ISAR) imaging for a complex, electrically large target is presented. In this context, physical optics (PO) is used as a high-frequency scattered field prediction technique and compressive sensing (CS) theory is applied to ISAR imaging by exploiting the sparse point scatterer structure of the target. Results prove that CS provides high resolution and accuracy in ISAR imaging even under the Nyquist sampling rate and linearly decreases computational time.

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.

Statistical modeling of wind turbines' bi-static free-space scattering characteristics for UHF-band applications

The main objective of this paper is to examine the statistical characteristics of and to explore the most appropriate fitting distribution model for the bi-static free-space scattering (BSFSS) caused by wind turbines (WTs). Based on the results of a previous work on the mono-static scattering case and the geometrical tendency and similarity of the observed probability densities to those of Students t, Gaussian and Laplacian distributions, the investigation has provided a benchmark focusing on the appropriateness of these three parametric distribution models on the BSFSS distribution of a WT obtained via a afore-presented analytical signal model.

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.

A statistical-based examination on wind turbines’ bi-static scatterings at 1 GHz frequency

Abstract This paper aims to introduce a statistical modeling approach for wind turbines’ (WTs) bi-static scattering characteristics in free-space environments. By using the analytical signal model proposed in the literature for the complex bi-static scattering of WTs, the statistical features of the long-term magnitude and phase variation for different bi-static scenarios (i.e. several discrete receiver locations for a fixed-point transmitter) are characterized with the help of the distribution-fitting process for several probability densities via maximum-likelihood estimation. The results of distribution-fitting process are interpreted with the help of goodness-of-fit considerations such as minimum square error, Kolmogorov–Smirnov Test, Kuiper’s V Test, and Watson’s Test, in order to qualify the suitability of each considered probability density. Moreover, by carrying out the examination by including a polynomial series approximation approach on the estimated parameters of two best-matching densities (i.e. -stable and Student’s t), the outputs are rendered as to be valid for any receiver locations. In addition to the insightful outcomes and conclusions for the considered electronic system scenario including the operating frequency (i.e. 1 GHz) and orientations, the investigation presented in this paper also introduces a simple, fast-responding methodology of data pool generation, distribution-fitting and performance assessment for the WTs’ bi-static scattering effects.

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.