Dorina Kabakchieva

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.

Experimental parameter estimation of vehicles GPS shadows by Forward scattering systems

In this paper, we conduct an experimental study with the aim to obtain a large number of estimates of various parameters characterizing the GPS shadows created by three types of moving vehicles. The experimental cars have a similar size and therefore, the obtained estimates have similar values. From the results, it can be seen that the cars can be differentiated by means of their shadow parameters.

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.

Air target detection using pulsar FSR

The feasibility of Forward Scatter Radar (FSR) that exploits pulsars as transmission sources for the purpose of air targets detection is examined. We theoretically calculated the power budget for air target detection using such a FSR system with the higher-gain radio telescopes used as receivers. The numerical results are obtained for three known radio telescopes and three pulsars.

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.

Joint CFAR detection and parameter estimation of different marine targets using Forward Scatter Radar

In this paper we research one joint structure of the CFAR detector and a parameter estimation of the moving different marine targets at the background of a sea clutter using Bistatic Forward Scatter Radar (FSR) system. In our investigates we use the two pulse MTI CFAR processor with K/M-L batch processor and parameter estimator for a marine target with unknown size are investigated on the base of real data records. The aim of the paper is to make statistical estimation of various target parameters [6-9]. These specific parameters necessary for Data Mining Classification have been obtained in the time and the frequency domains for different marine targets. The data itself have been gathered by the team of the Birmingham University using in-house developed FSR.