A method of feature selection in the aspect of specific identification of radar signals

This article presents an important task of classification, i.e. mapping surfaces which separate patterns in feature space in the scope of radar emitter recognition (RER) and classification. Assigning a tested radar to a particular class is based on defining its location from the discriminating areas. In order to carry out the classification process, it is necessary to define metrics in the feature space as it is essential to estimate the distance of a classified radar from the centre of the class. The method presented in this article is based on extraction and selection of distinctive features, which can be received in the process of specific emitter identification (SEI) of radar signals, and on the minimum distance classification. The author suggests a RER system which consists of a few independent channels. The task of each channel is to calculate the distance of the tested radar from a given class and finally, set the correct identification coefficient for each recognized radar. Thus, a multichannel system with independent distance measurement is obtained, which makes it possible to recognize particular radar copies. This system is implemented in electronic intelligence (ELINT) system and tested in real battlefield conditions

Fast-decision identification algorithm of emission source pattern in database

This article presents Fast-decision Identification Algorithm (FdIA) of Source Emission (SE) in DataBase (DB). The aim of this identification process is to define signal vector (V) in the form of distinctive features of this signal which is received in the process of its measurement. Superheterodyne ELectronic INTelligence (ELINT) receiver in the measure procedure was used. The next step in identification process is comparison vector with pattern in DB and calculation of decision function. The aim of decision function is to evaluate similarity degree between vector and pattern. Identification process mentioned above differentiates copies of radar of the same type which is a special test challenge defined as Specific Emitter Identification (SEI). The authors of this method drew up FdIA and three-stage parameterization by the implementation of three different ways of defining the degree of similarity between vector and pattern (called ’Compare procedure’). The algorithm was tested on hundreds of signal vectors coming from over a dozen copies of radars of the same type. Fast-decision Identification Algorithm which was drawn up and implemented makes it possible to create Knowledge Base which is an integral part of Expert DataBase. As a result, the amount of the ambiguity of decisions in the process of Source Emission Identification is minimized

Fast-decision identification algorithm of emission source pattern in database

This article presents Fast-decision Identification Algorithm (FdIA) of Source Emission (SE) in DataBase (DB). The aim of this identification process is to define signal vector (V) in the form of distinctive features of this signal which is received in the process of its measurement. Superheterodyne ELectronic INTelligence (ELINT) receiver in the measure procedure was used. The next step in identification process is comparison vector with pattern in DB and calculation of decision function. The aim of decision function is to evaluate similarity degree between vector and pattern. Identification process mentioned above differentiates copies of radar of the same type which is a special test challenge defined as Specific Emitter Identification (SEI). The authors of this method drew up FdIA and three-stage parameterization by the implementation of three different ways of defining the degree of similarity between vector and pattern (called ’Compare procedure’). The algorithm was tested on hundreds of signal vectors coming from over a dozen copies of radars of the same type. Fast-decision Identification Algorithm which was drawn up and implemented makes it possible to create Knowledge Base which is an integral part of Expert DataBase. As a result, the amount of the ambiguity of decisions in the process of Source Emission Identification is minimized

Modułowy Integrator do zarządzania systemem C4I żołnierza

The C4I (Command, Control, Communications, Computers and Intelligence) system is the integral part of soldier’s Individual Combat System. This subsystem establishes new standards by providing soldiers with supporting information and therefore improving their knowledge. For that reason, the C4I system integrates all radio, electronic and optoelectronic & IT components, which are a part of the soldiers equipment. The C4I system ensures exchange of data, imagery, and audio between its users and elements of its environment. For the above functionality to be implemented, the C4I subsystem needs a device which will integrate the above-mentioned functionality in its configuration. The Modular Integrator is this kind of device. This paper describes the design and the functionality of the Modular Integrator used for soldier’s C4I system management. The paper focuses on the design of the device and on the set of functions it can perform in the C4I system. An innovative concept, involving integration of three separate devices in a single enclosure, i.e. a personal radio, a portable digital assistant and a mobile phone with a power supply system allows the Modular Integrator to be referred to as a device with “functional multiformity”. A key aspect of the project implementation was also the adaptation of the device to psychological & physical attributes of its future users. This adaptation was accomplished through evaluation of the Modular Integrator in terms of ergonomics, technical aesthetics, anthropometrics and customization, user interaction, pressure force, thermal comfort and lateralization. This “3-in-1” researched and tested solution, developed to the 9th technology readiness level, has no match on national and on international markets.Integralnym elementem Indywidualnego Systemu Walki żołnierza jest podsystem C4I. Podsystem ten, wprowadza nową jakość jaką jest wsparcie żołnierza w informacje, a tym samym w wiedzę. Z tego powodu C4I integruje wszystkie elementy radiowe, elektroniczne oraz optoelektroniczne i informatyczne będące na wyposażeniu żołnierza oraz zapewnia wymianę danych, obrazów i fonii pomiędzy użytkownikiem i elementami jego otoczenia. Aby powyższe funkcje mogły być realizowane, podsystem C4I musi zawierać w swojej konfiguracji urządzenie, które zintegruje opisane wyżej funkcjonalności. Takim urządzaniem jest Modułowy Integrator. Niniejszy artykuł opisuje projekt budowy oraz funkcjonalność Modułowego Integratora do zarządzania systemem C4I żołnierza. W artykule zwrócono szczególną uwagę na budowę tego urządzenia oraz zbiór funkcji jakie urządzenie to realizuje w podsystemie C4I. Innowacyjny pomysł polegający na integracji w pojedynczej obudowie trzech różnych urządzeń, tj.: radiostacji osobistej, przenośnego komputera osobistego oraz systemu zasilania, realizowany na IX poziomie gotowości technologii, pozwolił określić Modułowy Integrator mianem urządzenia o „funkcjonalnej wielopostaciowości”. Kluczowym aspektem realizacji projektu było również dostosowanie urządzenia do cech psychofizycznych przyszłych użytkowników. Powyższe dostosowanie zostało zrealizowane poprzez ocenę Modułowego Integratora w aspektach ergonomii, estetyki technicznej, antropometrii i personalizacji, interakcji z użytkownikiem, siły nacisku, komfortu termicznego oraz lateralizacji. Opracowane, przebadane oraz przetestowane rozwiązanie „3 in 1” nie ma swojego odpowiednika na rynku krajowym jak i na rynkach zagranicznych

Optimizing the minimum cost flow algorithm for the phase unwrapping process in SAR radar

The last three decades have been abundant in various solutions to the problem of Phase Unwrapping in a SAR radar. Basically, all the existing techniques of Phase Unwrapping are based on the assumption that it is possible to determine discrete ”derivatives” of the unwrapped phase. In this case a discrete derivative of the unwrapped phase means a phase difference (phase gradient) between the adjacent pixels if the absolute value of this difference is less than π. The unwrapped phase can be reconstructed from these discrete derivatives by adding a constant multiple of 2π. These methods differ in that the above hypothesis may be false in some image points. Therefore, discrete derivatives determining the unwrapped phase will be discontinuous, which means they will not form an irrotational vector field. Methods utilising branch-cuts unwrap the phase by summing up specific discrete partial derivatives of the unwrapped phase along a path. Such an approach enables internally cohesive results to be obtained. Possible summing paths are limited by branch-cuts, which must not be intersected. These branch-cuts connect local discontinuities of discrete partial derivatives. The authors of this paper performed parametrization of the Minimum Cost Flow algorithm by changing the parameter determining the size of a tile, into which the input image is divided, and changing the extent of overlapping of two adjacent tiles. It was the basis for determining the optimum (in terms of minimum Phase Unwrapping time) performance of the Minimum Cost Flow algorithm in the aspect of those parameters

Specific emitter identification based on graphical representation of the distribution of radar signal parameters

The article presents some possibilities of same type radar copies identification with the use of graphical representation. The procedure described by the authors is based on transformation and analysis of basic parameters distribution which are measured by the radar signal especially Pulse Repetition Interval. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. Information such as Pulse Repetition Interval, Angle of Arrival, Pulse Width, Radio Frequency and Doppler shifts are not usable. The most important objectives are to determine the number of emitters present and classify incoming pulses according to emitters. To classify radar emitters and precisely identification the copy of the same type of an emitter source in surrounding environment, we need to explore the detailed structure i.e. intra-pulse information, unintentional radiated electromagnetic emission and fractal features of a radar signal. An emitter has its own signal structure. This part of radar signal analysis is called Specific Emitter Identification. Utilization of some specific properties of electronic devices can cause heightening probability of a correct identification

Identification of emitter sources in the aspect of their fractal features

This article presents the procedure of identification radar emitter sources with the trace distinctive features of original signal with the use of fractal features. It is a specific kind of identification called Specific Emitter Identification, where as a result of using transformations, which change measure points, a transformation attractor was received. The use of linear regression and the Lagrange polynomial interpolation resulted in the estimation of the measurement function. The method analysing properties of the measurement function which has been suggested by the authors caused the extraction of two additional distinctive features. These features extended the vector of basic radar signals’ parameters. The extended vector of radar signals’ features made it possible to identify the copy of radar emitter source

Optimizing the minimum cost flow algorithm for the phase unwrapping process in SAR radar

The last three decades have been abundant in various solutions to the problem of Phase Unwrapping in a SAR radar. Basically, all the existing techniques of Phase Unwrapping are based on the assumption that it is possible to determine discrete ”derivatives” of the unwrapped phase. In this case a discrete derivative of the unwrapped phase means a phase difference (phase gradient) between the adjacent pixels if the absolute value of this difference is less than π. The unwrapped phase can be reconstructed from these discrete derivatives by adding a constant multiple of 2π. These methods differ in that the above hypothesis may be false in some image points. Therefore, discrete derivatives determining the unwrapped phase will be discontinuous, which means they will not form an irrotational vector field. Methods utilising branch-cuts unwrap the phase by summing up specific discrete partial derivatives of the unwrapped phase along a path. Such an approach enables internally cohesive results to be obtained. Possible summing paths are limited by branch-cuts, which must not be intersected. These branch-cuts connect local discontinuities of discrete partial derivatives. The authors of this paper performed parametrization of the Minimum Cost Flow algorithm by changing the parameter determining the size of a tile, into which the input image is divided, and changing the extent of overlapping of two adjacent tiles. It was the basis for determining the optimum (in terms of minimum Phase Unwrapping time) performance of the Minimum Cost Flow algorithm in the aspect of those parameters

Fractal Features of Specific Emitter Identification

This article presents the issues connected with emitter sources identification with low distinctive primary features of a signal. It is a specific type of identification called specific emitter identification, which distinguishes different copies of the same type of emitter. The term of specific emitter identification was presented on the basis of fractal features received from the transformation of measurement data sets. The use of linear regression and Lagrange polynomial interpolation resulted in the estimation of measurement function. The method analysing properties of measurement function which was suggested by the authors caused the extraction of two additional distinctive features. The features above extended the vector of basic radar signals' parameters. The extended vector of radar signals' features made it possible to identify the copy of emitter source