Browsing by Author "Pakfiliz, Ahmet Gungor"

Now showing 1 - 6 of 6

Increasing self-protection jammer efficiency using radar cross section adaptation
(2022) Pakfiliz, Ahmet Gungor; 0000-0002-5901-228X
Increasing jamming effectiveness in self-protection electronic warfare systems is a crucial research area for air operations. Of all the factors affecting jamming performance, two parameters that increase jamming efficiency without changing the design parameters of radar and jammer systems are RCS and distance. Changing the distance between radar and target to increase the jamming effect is not sensible. Because disobeying the path for distance concern may ruin the operation aim. Adapting the RCS to the situation appears to be a suitable method to increase the jamming efficiency. In this study, a novel method is proposed for enhancement the jamming effectiveness by changing RCS appropriately. Hence, a heading calculator unit is integrated into the self-protection electronic warfare system. The heading calculator interacts with the radar warning receiver and jammer and points an appropriate heading direction during jamming. The heading variances are limited to as slight changes as possible.
A New Method for Surface-to-Air Video Detection and Tracking of Airborne Vehicles
(2022) Pakfiliz, Ahmet Gungor; https://orcid.org/0000-0002-5901-228X
Detection process of airborne targets may be thought simple because of the incompatible nature of aircraft, choppers, UAVs, and drones regarding clear sky background. When changes in the background are considered, brightness variation of the sky complicates the process. Changes in the shapes and types of clouds add another challenge to the process. Tracking process directly depends on the detection process and type of the data stream. The practical systems used for video detection and tracking of airborne targets are manual, and manual structures have some drawbacks compared to automatic structures. For video surveillance, guidance, regional security, and defense applications in dense environments, automatic detection and tracking process may be an obligation rather than preference. In this study, an automatic detection and tracking algorithm for video streams of airborne targets is proposed. A land-based moving camera captures the video data, and not only the flying objects but probably also the camera are in motion. Although the detection and tracking of moving objects via moving sensors is a relatively arduous task, this is the prevalent case in real-life scenarios. Video detection and tracking systems have one or more moving video sensors, while one or more flying air vehicles are in operation area. The proposed algorithm includes an image processing stage for detection and a tracking stage for initiation and continuation. An assessment study has been conducted for the actual video data and found that the proposed method yields successful results for detection, track formation, and continuation processes.
A novel approach for automatic detection and tracking of flying objects
(2019) Pakfiliz, Ahmet Gungor
In this study, a new method is presented to automatically detect and track flying objects through video systems that are used for surface to air tracking tasks. In this approach, a method has been developed in which Standard Deviation is used to determine the presence of a flying object. The measurement data is adapted to track, so that the flying object becomes more dominant than the background. In order to track the detected target in real time, Interacting Multiple Model Probabilistic Data Association with Amplitude Information (IMMPDA-AI) algorithm is used. Although the IMMPDA-AI algorithm is mainly a point tracking algorithm, in this study, its applicability to video tracking is shown. For this purpose, the amplitude information of the sampled video frames is encoded as point data and the tracking is performed on this data. Thus, an algorithm has been developed in which the target is automatically detected, track initiated and continued. The algorithm is evaluated for different maneuvers, target types and clutter situations, and successful results are obtained.
PRI Deinterleaving with Connected Component Labeling-Based Clustering
(2023) Fisne, Neslihan; Pakfiliz, Ahmet Gungor; 0000-0002-5901-228X; HSH-4659-2023
Air platforms use radar warning receiver systems to detect threat radars in military operations. It is critical for an aircraft operating in a hostile environment to detect and classify radar signals and determine radar emitters' identity and capability. This study proposes an innovative solution to the signal separation problem for pulse radar signals by the computer vision-based connected component labeling method. With the proposed solution, clustering is performed automatically on the three-dimensional image matrix created by using the parameters of the arrival angle, radio frequency, pulse width of the signals. After clustering, the pulse repetition interval (PRI) deinterleaving is provided by the time of arrival analysis in each cluster. Also, various simulations were carried out using different synthetic radar datasets containing pulse-on-pulse states. In the simulations, PRI accuracy analyzes were performed.
Reducing Processing Time for Histogram PMHT Algorithm in Video Object Tracking
(2018) Pakfiliz, Ahmet Gungor
This paper describes a novel approach for reducing the processing time of the histogram probabilistic multi-hypothesis tracker (H-PMHT) algorithm in video applications. Video data of flying vehicles is taken from surface to air, and a temporal difference-based technique is applied to video frames for meeting the intensity demands of H-PMHT algorithm. This technique also enables discrimination between objects and eliminates clutter. Variations between the structures of the standard and the improved version of H-PMHT algorithms are described. In addition, the improved H-PMPT is compared with the standard H-PMHT and another approved tracking algorithm to evaluate the performance and processing time reduction ratings.
Square root central difference-based FastSLAM approach improved by differential evolution
(2016) Ankishan, Haydar; Ari, Fikret; Tartan, Emre Oner; Pakfiliz, Ahmet Gungor
This study presents a new approach to improve the performance of FastSLAM. The aim of the study is to obtain a more robust algorithm for FastSLAM applications by using a Kalman filter that uses Stirling's polynomial interpolation formula. In this paper, some new improvements have been proposed; the first approach is the square root central difference Kalman filter-based FastSLAM, called SRCD-FastSLAM. In this method, autonomous vehicle (or robot) position, landmarks' position estimations, and importance weight calculations of the particle filter are provided by the SRCD-Kalman filter. The second approach is an improved version of the SRCD-FastSLAM in which particles are improved by a differential evolution (DE) algorithm for reducing the risk of the particle depletion problem. Simulation results are given as a comparison of FastSLAM II, unscented (U)-FastSLAM, SRCD-Kalman filter-aided FastSLAM, SRCD particle filter-based FastSLAM, SRCD-FastSLAM, and DE-SRCD-FastSLAM. The results show that SRCD-based FastSLAM approaches accurately compute mean and precise uncertainty of the robot position in comparison with FastSLAM II and U-FastSLAM methods. However, the best results are obtained by DE-SRCD-FastSLAM, which provides significantly more accurate and robust estimation with the help of DE with fewer particles. Moreover, consistency of the DE-SRCD-FastSLAM is more prolonged than that of FastSLAM II, U-FastSLAM, and SRCD-FastSLAM.