Askin Demirkol

Real time tracking of TV satellites on moving vehicles using Kalman filter

This paper presents a real time tracking of TV satellites on mobile vehicles using Kalman filter. Kalman filter is adapted to tracking the satellite for uninterrupted broadcasting on moving vehicles. Information about the real time position of the vehicle and satellite is gathered with the aid of onboard sensors on an android device and then analyzed on an android based application. A real time application has been implemented continuously to calculate the azimuth and elevation angle of the satellite. Accelerometer and magnetometer sensors are used for collecting the information on the position of the antenna. GPS sensor is used for collecting the latitude and longitude data of the vehicle. These data are then used for calculating the azimuth and elevation angle of a parabolic dish antenna.

Estimation of target density functions in time-varying environments

In this article, two different solutions are presented to the problem of estimation of time-varying range-angle in nonstationary environments. The first one utilizes a wideband signal with a certain bandlimitedness condition on the time variation of the target density function at each point in space. By choosing small successive processing time intervals, bandlimitedness of the time variation becomes much more realistic than assuming no time time variation or a specific time variation. The second one does not have this assumption and utilizes a narrowband signal with a continuous spectral content. Both provide a general methodology for imaging of nonstationary environments. In addition to nonstationarity, time-varying target density functions can also describe nonlinearity of a medium where each point distorts the transmitted wave by multiplying it with a time-varying function during reflection.

Performance analysis of image reconstruction based on Modified Radon Transform with Wavelet Transform

In many medical imaging modalities, image reconstruction is a key component. Radon Transform is mostly used for reconstruction due to its ability to map from the image space to line parameter space. The goal of this work is to perform another alternative to remove blurring on reconstructed images which is obtained by the Classic Radon Transform. Wavelet Transform, which provides time and frequency representation, allows complex information such as images to be separated into elementary forms at different positions and scales and reconstructed with good precision. In consequence, we have an additional chance to distinguish image content and noise. By combining benefits both Radon Transform and Wavelet Transform, it becomes one of many ways to reconstruct an image. In addition, filters are applied on reconstructed images to improve their performance. The quality of the images is evaluated by Peak Signal Noise to Ratio (PSNR) and Structural Similarity (SSIM) to find the performance of the reconstructed images. Numerical results show that the Modified Radon Transform with Wavelet Transform plus wiener filter is more effective than the Classic Radon Transform.

Multi-targets tracking using parallel Kalman filter

The multi target tracking problem is considered in this paper and a parallel kalman filter algorithm (Decentralized Kalman Filter) is presented. The proposed algorithm designed to make early and more accurate estimate for dynamic multi-targets in the area and to determine whether the sensor data representing the same targets or not, when the ability to track targets is essential in missile defense. Proposed technique is a two stage data processing technique which processes data from multi sensor system. In the first stage, each local processor uses its own data to make a best local estimate using standerd kalman filter and then these estimates are then obtained in parallel processing mode to make best global estimate. The proposed model is tested when two sensors are used to track three targets and any sensor tracked two targets. The proposed technique performance is evaluated using measures such as the error covariance matrix and it gived high accuricy and optimal estimate. The experimental results showed that the proposed method has the ability to determine whether these two targets detected by the first sensor are the same two targets detected by the second sensor or two new targets.

Reconstruction of a radar target density function using radon transform

In this paper a new method to obtain a target density function with variables range and scanning angle, by using theoretical background of the Radon transform that is widely used in tomographic scanning is presented. In order to acquire the backscattered signal from diverse angles, phased array radar system is employed. Effectiveness, validity and the place in the literature of this new approach are considered both in theoretical and practical aspects. The formulation, geometry and implementation results are achieved. The new method is compared with previous methods.

A beamforming approach in archaeogeophysical imaging

In this study, the contribution of the geophysics method as resistivity and beamforming approach to the archaeogeophysics were investigated. Recently, the resistivity and magnetic have been used to image the archaeological structures. In this work, the modified case of the resistivity to the beamforming was studied for imaging. It was believed that, the buried archaeological structures could be imaged using the beamforming approach as well as the present geophysic methods. Once an implementation of the resistivity was made in Adremytteion ancient city in the Balikesir, it was observed that the use of the beamforming would be capable of making a meaningful contribution to the analysis of the structures in the area.

A systematıc approach to power systems

In this study, a general approach for modeling power systems is discussed. The power system model, handled in this paper, is based on observation with sensors distributed in space. To estimate the dinamic parameters of power systems in the coverage of each sensors, makes control and keep track of system possible. For this purpose, the sensors and power systems which are organized by a distributed network structure, predisposed to a stochastic model called decentralized Kalman filter. Thus, the accurate estimation and measure of system outputs are aimed. Accordingly, a theoretic model has been proposed that provides a general impression and formulation of power systems.

A Holographic Approach to Image Processing

In this document, a distributed image processing based on the mathematics of holographic storage is presented. This work was inspired by the distributed principles between wave propagation and subsequent interference patterns seen in holograms. We then analyzed the mathematics to produce a general mathematical description of the holographic process. From a generalized case of this analysis we are able to propose this way for works in some engineering fields such as radar, control and neural networks.