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    Tytuł:
    Identification of emitter sources in the aspect of their fractal features
    Autorzy:
    Dudczyk, J.
    Kawalec, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/200415.pdf
    Data publikacji:
    2013
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    fractal feature
    pattern of radar
    signal processing
    Specific Emitter Identification (SEI)
    Opis:
    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.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2013, 61, 3; 623-628
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Applying the radiated emission to the specific emitter identification
    Autorzy:
    Dudczyk, J.
    Wnuk, M.
    Matuszewski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/309391.pdf
    Data publikacji:
    2005
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    radiated emission
    distance function
    homology function
    measurement and signature intelligence
    specific emitter identification
    Opis:
    During the last years we have observed fast development of the electronic devices and electronic warfare systems (EW). One of the most principal functions of the ESM/ELINT system is gathering basic information from the entire electromagnetic spectrum and its analysis. Simultaneously, utilization of some tools of artificial intelligence (AI) during the process of emitter identification is very important too. A significant role is played by measurement and signature intelligence (MASINT) based on non-intentional emission (calls-radiated emission). This emission is a source of knowledge about an analysed emitter due to its incidental "chemical", "spectral" traces and non-communication emitter's characteristics. The process of specific emitter identification (SEI) based on extraction of distinctive radiated emission features is presented by the authors. Specially important is utilization of a database (DB) in the process of identifying a detectable radar emission.
    Źródło:
    Journal of Telecommunications and Information Technology; 2005, 2; 57-60
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fast-decision identification algorithm of emission source pattern in database
    Autorzy:
    Dudczyk, J.
    Kawalec, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/199845.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Fast-decision Identification Algorithm
    Specific Emitter Identification
    Emitter Pattern
    database
    superheterodyne ELINT receiver
    baza danych
    superheterodynowy odbiornik ELINT
    algorytm FdIA
    Opis:
    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.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 2; 385-389
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    A method of feature selection in the aspect of specific identification of radar signals
    Autorzy:
    Dudczyk, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/200837.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    radar emitter recognition
    RER
    specific emitter identification
    SEI
    minimum distance classification
    ELINT system
    klasyfikator minimalnej odległości
    System ELINT
    Opis:
    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.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2017, 65, 1; 113-119
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Specific emitter identification based on graphical representation of the distribution of radar signal parameters
    Autorzy:
    Dudczyk, J.
    Kawalec, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/200468.pdf
    Data publikacji:
    2015
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    Specific Emitter Identification
    SEI
    radar recognition
    ELINT system
    Electronics Warfare System
    EWS
    radar
    system ELINT
    system EWS
    Opis:
    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.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2015, 63, 2; 391-396
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Specific emitter identification using geometric features of frequency drift curve
    Autorzy:
    Zhao, Y.
    Wui, L.
    Zhang, J.
    Li, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/200575.pdf
    Data publikacji:
    2018
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    specific emitter identification
    geometric features
    frequency drift
    adaptive fractional spectrogram
    support vector machine
    emiter
    cechy geometryczne
    dryf częstotliwości
    spektrogram
    Opis:
    Specific emitter identification (SEI) is a technique for recognizing different emitters of the same type which have the same modulation parameters. Using only the classic modulation parameters for recognition, one cannot distinguish different emitters of a same type. To solve the problem, new features urgently need to be developed for recognition. This paper focuses on the common phenomenon of frequency drift, defines geometric features of frequency drift curve and, finally, proposes a practical algorithm of specific emitter identification using the geometric features. The proposed algorithm consists of three processes: instantaneous frequency estimation based on the adaptive fractional spectrogram, feature extraction of frequency drift curve based on geometric methods for describing a curve and recognition process based on support vector machine. Simulation results show that the identification rate is generally more than 98% above –5 dB of signal to noise ratio (SNR), and real data experiment verifies the practical performance of the proposed algorithm.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 1; 99-108
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep adversarial neural network for specific emitter identification under varying frequency
    Autorzy:
    Huang, Keju
    Yang, Junan
    Liu, Hui
    Hu, Pengjiang
    Powiązania:
    https://bibliotekanauki.pl/articles/2173603.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    specific emitter identification
    unsupervised domain adaptation
    transfer learning
    deep learning
    identyfikacja emitera konkretna
    adaptacja domeny nienadzorowana
    transfer uczenia się
    uczenie głębokie
    Opis:
    Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; art. no. e136737
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep adversarial neural network for specific emitter identification under varying frequency
    Autorzy:
    Huang, Keju
    Yang, Junan
    Liu, Hui
    Hu, Pengjiang
    Powiązania:
    https://bibliotekanauki.pl/articles/2128144.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    specific emitter identification
    unsupervised domain adaptation
    transfer learning
    deep learning
    identyfikacja emitera konkretna
    adaptacja domeny nienadzorowana
    transfer uczenia się
    uczenie głębokie
    Opis:
    Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 2; e136737, 1--9
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Specyficzna identyfikacja emiterów radarowych bazująca na analizie składowych głównych
    Specific radar emitter identification based on principal component analysis
    Autorzy:
    Kawalec, A.
    Owczarek, R.
    Rapacki, T.
    Wnuczek, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/210693.pdf
    Data publikacji:
    2006
    Wydawca:
    Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
    Tematy:
    walka elektroniczna
    klasyfikacja cech sygnałów radarowych
    specyficzna identyfikacja emiterów
    przekształcenie Karhunena-Loeve'a
    warfare electronic
    radar signal feature classification
    specific emitter identification
    Karhunen-Loeve expansion
    Opis:
    W artykule została przedstawiona problematyka związana z identyfikacją emiterów radarowych należących do tego samego typu i klasy. Jest to specyficzny rodzaj identyfikacji (SEI, ang. Specific Emitter Identification), polegający na rozróżnianiu poszczególnych egzemplarzy tego samego typu radaru. Klasyczna identyfikacja sygnałów bazująca na analizie statystycznej podstawowych parametrów mierzalnych sygnału nie spełnia wymagań stawianych przed SEI. Przedstawiona w artykule metoda identyfikacji opiera się na przekształceniu Karhunena-Loeve'a (KL), która należy do metod analizy składowych głównych (PCA, ang. Principal Component Analysis).
    One of the most difficult tasks in the radar signal processing is optimal features extraction and classification. The multifunction radar systems cannot be classified and precisely recognized by most of new and modern Electronic Support Measure and Electronic Intelligence Devices in the real time. In most cases, the modern ESM/ELINT systems cannot recognize the different devices of the same type or class. New method of the radar identification with a high quality of recognizing is the Specific Emitter Identification (SEI). The main task is to find non-intentional modulations in the receiving signals. This paper provides an overview of the new methods of measurement emitter signal features parameters and their transformation. This paper presents some aspects of radar signal features processing using Karhunen-Loeve's expansion as a feature selection and classification transform.
    Źródło:
    Biuletyn Wojskowej Akademii Technicznej; 2006, 55, 1; 41-54
    1234-5865
    Pojawia się w:
    Biuletyn Wojskowej Akademii Technicznej
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Specific emitter identification based on one-dimensional complex-valued residual networks with an attention mechanism
    Autorzy:
    Qu, Lingzhi
    Yang, Junan
    Huang, Keju
    Liu, Hui
    Powiązania:
    https://bibliotekanauki.pl/articles/2086889.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    complex-valued residual network
    specific emitter identification
    fingerprint characteristic
    attention mechanism
    one-dimensional convolution
    sieć rezydualna o złożonej wartości
    specyficzna identyfikacja emiterów
    charakterystyka linii papilarnych
    mechanizm uwagi
    splot jednowymiarowy
    Opis:
    Specific emitter identification (SEI) can distinguish single-radio transmitters using the subtle features of the received waveform. Therefore, it is used extensively in both military and civilian fields. However, the traditional identification method requires extensive prior knowledge and is time-consuming. Furthermore, it imposes various effects associated with identifying the communication radiation source signal in complex environments. To solve the problem of the weak robustness of the hand-crafted feature method, many scholars at home and abroad have used deep learning for image identification in the field of radiation source identification. However, the classification method based on a real-numbered neural network cannot extract In-phase/Quadrature (I/Q)-related information from electromagnetic signals. To address these shortcomings, this paper proposes a new SEI framework for deep learning structures. In the proposed framework, a complex-valued residual network structure is first used to mine the relevant information between the in-phase and orthogonal components of the radio frequency baseband signal. Then, a one-dimensional convolution layer is used to a) directly extract the features of a specific one-dimensional time-domain signal sequence, b) use the attention mechanism unit to identify the extracted features, and c) weight them according to their importance. Experiments show that the proposed framework having complex-valued residual networks with attention mechanism has the advantages of high accuracy and superior performance in identifying communication radiation source signals.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 5; e138814, 1--10
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-10 z 10

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