Vera Behar

Study of moving target shadows using passive Forward Scatter radar systems

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.

Air target detection with a GPS forward-scattering radar

Forward scattering radar (FSR), as a specially configured bistatic radar, is provided with the capabilities of air target detection by using the GPS signal shadow. This paper discusses the experimental results obtained by GPS receiver near to Sofia airport. This research aims to demonstrate the feasibility of the Forward Scattering GPS system to detect air targets.

Comparative analysis of object shadows obtained by GPS and sound signals

The paper explores the shadows created by moving humans in two types of Forward Scatter (FS) systems, which use GPS signals and sound signals. The comparative analysis of results is based on the correlation analysis and focused on establishing of relationships between different types of shadows. The results show whether it is possible to use sound barriers for detection of moving objects.

Signal processing of GPS radio shadows formed by moving targets

This paper focuses on scientific issues related to new application of GPS in the construction and development of radar networks using the effect of Forward Scatter (FS) of electromagnetic waves for detection of objects by using of their GPS radio shadows. The paper theme is new, it combines two thematic areas: processing of GPS signals and processing of radar signals in FS radar systems.

Detection, parametric imaging and classification of very small marine targets emerged in heavy sea clutter utilizing GPS-based Forward Scattering Radar

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.

Data mining classification of cars based on GPS shadows in Forward Scatter Radar systems

The goal of this paper is to introduce a new concept - using Data Mining approach for radar classification of vehicles, based on their GPS shadow signals detected in a GPS L1-based Forward Scatter Radar (FSR) system. Real data is used for the current experiments, recording the GPS radio shadows of several moving vehicles with commercial non-professional GPS equipment. The records are further processed in MATLAB computational environment in order to obtain the estimated parameters of the GPS shadows. Data Mining approach is then implemented for classifying the cars, comparing classifiers generated with different classification algorithms - a decision tree, a neural network, and a Bayesian classifier, for two different variants of the class variable, taking three or two possible values. The best results are achieved for the two values of the class variable and the best performing classifiers are the neural networks.

Time of arrival estimation in pulsar-based navigation systems

This paper focuses on the Time of Arrival (TOA) estimation problem related to new application of pulsar signals for airplane-based navigation. The aim of the paper is to propose and evaluate a possible algorithm for TOA estimation that consists of epoch folding, filtering, CFAR detection, cross-correlation and TOA calculation. The TOA estimation algorithm proposed is verified using real experimental data obtained from the Westerbork radio observatory in The Netherlands. The performance of the proposed TOA algorithm is evaluated in terms of SNR at the cross-correlator input and the TOA accuracy.

Improvement in SNR of signal detection using filtering in pulsar-based navigation systems

This paper focuses on application of pulsar signals for pulsar-based navigation. The aim of the study is to present a possible algorithm for signal processing of pulsar signals, which consists of epoch folding, filtering and detection, and evaluate it using the real experimental data obtained from the radio observatory Dwingeloo, the Netherlands.

Experimental Study of Moving Man Detection by Acoustic Forward Scattering Radar System

The article explores the possibility of detection of people on the base of their sound shadow (sound blocking) when people cross the baseline in the Acoustic Forward Scatter Radar System (AFSRS). Experimental sound shadows have been obtained from moving people crossing AFSRS at different sound frequencies and different distances of people to the receiver. The sound shadow parameters of moving people, i.e. length of target shadow and peak Signal-to-Noise Ratio have been evaluated. The algorithm under investigation can be applied to create a network of sound barriers.

Feasibility of asteroid detection using pulsar FSR-network

The feasibility of asteroid detection using the Forward Scatter Radar Network that exploits three pulsars as transmitters is examined. We provide a power budget estimate for asteroid detection using such a FSR-network with the radio telescope used as a receiver. The numerical results are obtained for two known radio telescopes in the Netherland and three known pulsars.