The Global Positioning System (GPS) allows the accurate positioning of an object using satellite signals. There are a lot of applications of this technology in many scientific fields all over the world. The paper focuses on scientific issues related to new application of GPS technology using the effect of Forward Scattering (FS) of electromagnetic waves to detect targets by their GPS radio shadows. The aim of the paper is to make experimental studies of GPS radio shadows of vehicle targets irradiated by GPS signals and to develop algorithm for automatic detection of the shadows of these targets. In this paper, we offer an original approach for using of CFAR techniques for detection of moving targets from GPS radio shadows. The results show that from GPS radio shadows of different objects can be extracted information for classification of objects aimed at transport management or security of zones.

In this paper, we address a technique and related algorithms for precise detection, parametric imaging and classification of small marine targets in a harsh sensing environment attributed for heavy sea clutter via noncooperative processing of the GPS-based Forward Scatter Radar (FSR) data. In contrary to GPS L5 detection approach, the proposed technique utilizes civil GPS L1 signal formats in FSR exploiting GPS as a non-cooperative transmitter. In our previous studies it is shown that the use of the new power GPS signal L5, and the Forward Scattering effect providing a high SNR, at the detector input allows reliably to detect small air targets in conditions of the intense interference. In this paper we propose another approach, to enhance SNR, at the input of the detector in Forward Scattering Radar (FSR). The use of the effective filter (Local Variance Filter) for suppression of intensive sea clutter allows FSR reliably to detect small marine targets emerged in harsh sea clutter, but with GPS L1 signal, whose SNR is very small. At the classification level, the data mining approach is adopted, in which the target feature parameters are extracted from the preliminary filtered signals by utilizing the modified structure of a processor for target detection and parameter estimation in the time domain. Both, the decision tree-based and the neural network classifiers are featured and adapted for real-time implementation. The efficiency of the proposed technique is verified via analytical performance evaluations and experimental demonstrations.

Forward Scatter GPS (FS-GPS) radio shadows formed by different moving objects are investigated in this article. FS radio shadow is essential physical phenomenon, which can be used to extract some useful information about the objects that generate it. Registration of FS-GPS radio shadows from moving targets is performed using a small commercial GPS antenna and stationary receiver. Topology of the experiment meets the requirements for the appearance of the FS effect. The results presented in this article show that from FS-GPS radio shadows of different objects can be extracted information about the parameters of the object (size, speed and direction of movement, distance to the receiver). The information obtained can be used in various applications like those in classic radar, including radio barriers, security, classification and identification of moving and stationary objects.

In this article the effectiveness of one of the main methods for assisting the information search in large data sets is analyzed, namely the method based on the social approach. This method analyzes the behavior of multiple users when searching for information, particularly the keywords they use. Based on the obtained results, the relevant algorithms in the searching engines of the organizations can be optimized. The studies are based on data extracted from the databases of two relatively large information systems based at the University in Library Studies and Information Technologies in Sofia, Bulgaria and University of Economics in Varna, Bulgaria. The numerical and experimental results are analyzed and discussed.

The unauthorized widely used unmanned aerial vehicle UAV (drones) raises serious concerns about the security of infrastructure and people in the settlements. This expansion leads to the need for development of effective systems for their detection and classification. In this article, a drone detection approach based on the detection of the Radio Frequency (RF) emitted during the communication between the drone and its remote controller, is proposed.