Deep Learning Techniques in Radar Emitter Identification

In the field of electronic warfare (EW), one of the crucial roles of electronic intelligence is the identification of radar signals. In an operational environment, it is very essential to identify radar emitters whether friend or foe so that appropriate radar countermeasures can be taken against them. With the electromagnetic environment becoming increasingly complex and the diversity of signal features, radar emitter identification with high recognition accuracy has become a significantly challenging task. Traditional radar identification methods have shown some limitations in this complex electromagnetic scenario. Several radar classification and identification methods based on artificial neural networks have emerged with the emergence of artificial neural networks, notably deep learning approaches. Machine learning and deep learning algorithms are now frequently utilized to extract various types of information from radar signals more accurately and robustly. This paper illustrates the use of Deep Neural Networks (DNN) in radar applications for emitter classification and identification. Since deep learning approaches are capable of accurately classifying complicated patterns in radar signals, they have demonstrated significant promise for identifying radar emitters. By offering a thorough literature analysis of deep learning-based methodologies, the study intends to assist researchers and practitioners in better understanding the application of deep learning techniques to challenges related to the classification and identification of radar emitters. The study demonstrates that DNN can be used successfully in applications for radar classification and identification.   &nbsp

De-interleaving of Radar Pulses for EW Receivers with an ELINT Application

De-interleaving is a critical function in Electronic Warfare (EW) that has not received much attention in the literature regarding on-line Electronic Intelligence (ELINT) application. In ELINT, on-line analysis is important in order to allow for efficient data collection and for support of operational decisions. This dissertation proposed a de-interleaving solution for use with ELINT/Electronic-Support-Measures (ESM) receivers for purposes of ELINT with on-line application. The proposed solution does not require complex integration with existing EW systems or modifications to their sub-systems. Before proposing the solution, on-line de-interleaving algorithms were surveyed. Density-based spatial clustering of applications with noise (DBSCAN) is a clustering algorithm that has not been used before in de-interleaving; in this dissertation, it has proved to be effective. DBSCAN was thus selected as a component of the proposed de-interleaving solution due to its advantages over other surveyed algorithms. The proposed solution relies primarily on the parameters of Angle of Arrival (AOA), Radio Frequency (RF), and Time of Arrival (TOA). The time parameter was utilized in resolving RF agility. The solution is a system that is composed of different building blocks. The solution handles complex radar environments that include agility in RF, Pulse Width (PW), and Pulse Repetition Interval (PRI)

MULTISTATIC RADAR EMITTER IDENTIFICATION USING ENTROPY MAXIMIZATION BASED INDEPENDENT COMPONENT ANALYSIS

Radar emitter identification is state-of-the-art in modern electronic warfare. Presently multistatic architecture is adapted by almost all the radar systems for better tracking performance and accuracy in target detection. Hence, identification and classification of radar emitters operating in the surveillance region are the major problems. To deal with the difficulty of identification of radar emitters in a complex electromagnetic environment, in this work entropy maximization method of Independent Component Analysis (ICA) based on gradient ascent algorithm is proposed. This algorithm separates unknown source signals from the interleaved multi-component radar signals. The discrete source signals are extracted from the multi-component signal by optimizing the entropy where maximum entropy is achieved using a gradient ascent approach through unsupervised learning. As better detection capability and range resolution are achieved by Linear Frequency Modulated (LFM) signals for radar systems here, multicomponent LFM signals with low SNR are considered as the signal mixture from which, the independent sources separated. A mathematical model of the algorithm for entropy maximization is illustrated in this paper. Simulation result validates the effectiveness of the algorithm in terms of time domain separation of the signal, and time-frequency analysi

Modulation recognition of low-SNR UAV radar signals based on bispectral slices and GA-BP neural network

In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs

Spectrum sensing for cognitive radio and radar systems

The use of the radio frequency spectrum is increasing at a rapid rate. Reliable and efficient operation in a crowded radio spectrum requires innovative solutions and techniques. Future wireless communication and radar systems should be aware of their surrounding radio environment in order to have the ability to adapt their operation to the effective situation. Spectrum sensing techniques such as detection, waveform recognition, and specific emitter identification are key sources of information for characterizing the surrounding radio environment and extracting valuable information, and consequently adjusting transceiver parameters for facilitating flexible, efficient, and reliable operation. In this thesis, spectrum sensing algorithms for cognitive radios and radar intercept receivers are proposed. Single-user and collaborative cyclostationarity-based detection algorithms are proposed: Multicycle detectors and robust nonparametric spatial sign cyclic correlation based fixed sample size and sequential detectors are proposed. Asymptotic distributions of the test statistics under the null hypothesis are established. A censoring scheme in which only informative test statistics are transmitted to the fusion center is proposed for collaborative detection. The proposed detectors and methods have the following benefits: employing cyclostationarity enables distinction among different systems, collaboration mitigates the effects of shadowing and multipath fading, using multiple strong cyclic frequencies improves the performance, robust detection provides reliable performance in heavy-tailed non-Gaussian noise, sequential detection reduces the average detection time, and censoring improves energy efficiency. In addition, a radar waveform recognition system for classifying common pulse compression waveforms is developed. The proposed supervised classification system classifies an intercepted radar pulse to one of eight different classes based on the pulse compression waveform: linear frequency modulation, Costas frequency codes, binary codes, as well as Frank, P1, P2, P3, and P4 polyphase codes. A robust M-estimation based method for radar emitter identification is proposed as well. A common modulation profile from a group of intercepted pulses is estimated and used for identifying the radar emitter. The M-estimation based approach provides robustness against preprocessing errors and deviations from the assumed noise model

Artificial intelligence methods for security and cyber security systems

This research is in threat analysis and countermeasures employing Artificial Intelligence (AI) methods within the civilian domain, where safety and mission-critical aspects are essential. AI has challenges of repeatable determinism and decision explanation. This research proposed methods for dense and convolutional networks that provided repeatable determinism. In dense networks, the proposed alternative method had an equal performance with more structured learnt weights. The proposed method also had earlier learning and higher accuracy in the Convolutional networks. When demonstrated in colour image classification, the accuracy improved in the first epoch to 67%, from 29% in the existing scheme. Examined in transferred learning with the Fast Sign Gradient Method (FSGM) as an analytical method to control distortion of dissimilarity, a finding was that the proposed method had more significant retention of the learnt model, with 31% accuracy instead of 9%. The research also proposed a threat analysis method with set-mappings and first principle analytical steps applied to a Symbolic AI method using an algebraic expert system with virtualized neurons. The neural expert system method demonstrated the infilling of parameters by calculating beamwidths with variations in the uncertainty of the antenna type. When combined with a proposed formula extraction method, it provides the potential for machine learning of new rules as a Neuro-Symbolic AI method. The proposed method uses extra weights allocated to neuron input value ranges as activation strengths. The method simplifies the learnt representation reducing model depth, thus with less significant dropout potential. Finally, an image classification method for emitter identification is proposed with a synthetic dataset generation method and shows the accurate identification between fourteen radar emission modes with high ambiguity between them (and achieved 99.8% accuracy). That method would be a mechanism to recognize non-threat civil radars aimed at threat alert when deviations from those civilian emitters are detected

Classifying low probability of intercept radar using fuzzy artmap

Electronic Support (ES) operations concern themselves with the ability to search for, intercept, track and classify threat emitters. Modern radar systems in turn aim to operate undetected by intercept receivers. These radar systems maintain Low Probability of Intercept (LPI) by utilizing low power emissions, coded waveforms, wideband operation, narrow beamwidths and evasive scan patterns without compromising accuracy and resolution. The term LPI refers to the small chance or likelihood of intercept actually occurring. The complexity and degrees of freedom available to modern radar place a high demand on ES systems to provide detailed and accurate real-time information. Intercept alone is not sufficient and this study focusses on the detection, feature extraction (parameter estimation) and classification (using Fuzzy ARTMAP), of the Pilot Mk3 LPI radar. Fuzzy ARTMAP is a cognitive neural method combining fuzzy logic and Adaptive Resonance Theory (ART) to create categories of class prototypes to be classified. Fuzzy ARTMAP systems are formed by self-organizing neural architectures that are able to rapidly learn and classify both discreet and continuous input patterns. To evaluate the suitability of a given ES intercept receiver against a particular LPI radar, the LPI performance factor is defined by combining the radar range, intercept receiver range and sensitivity equations. The radar wants to force an opposing intercept receiver into its range envelope. On the contrary, the intercept receiver would ideally want to operate outside the specified radar detection range to avoid being detected by the radar. The Maximum Likelihood (ML) detector developed for this study is capable of detecting the Pilot Mk3 radar, as it allows sufficient integration gain for detection beyond the radar maximum range. The accuracy of parameter estimation in an intercept receiver is of great importance, as it has a direct impact on the accuracy of the classification stage. Among the various potentially useful radar parameters, antenna rotation rate, transmit frequency, frequency sweep and sweep repetition frequency were used to classify the Pilot Mk3 radar. Estimation of these parameters resulted in very clear clustering of parameter data that distinguish the Pilot Mk3 radar. The estimated radar signal parameters are well separated to the point that there is no overlap of features. If the detector is able to detect an intercepted signal it will be able to make accurate estimates of these parameters. The Fuzzy ARTMAP classifier is capable of classifying the radar modes of the Pilot Mk3 LPI radar. Correct Classification Decisions (CCD) of 100% are easily achieved for a variety of classifier configurations. Classifier training is quite efficient as good generalisation between input and output spaces is achieved from a training dataset comprising only 5% of the total dataset. If any radar is LPI, there must be a consideration for the radar as well as the opposing intercept receiver. Calculating the LPI performance factor is a useful tool for such an evaluation. The claim that a particular radar is LPI against any intercept receiver is too broad to be insightful. This also holds for an intercept receiver claiming to have 100% Probability of Intercept (POI) against any radar. AFRIKAANS : Elektroniese ondersteuningsoperasies het ten doel om uitsendings van bedreigings te soek, te onderskep, te volg en ook te klassifiseer. Moderne radarstelsels probeer op hulle beurt om hul eie werk te verrig sonder om onderskep te word. Hierdie tipe radarstelsels handhaaf ’n Lae Waarskynlikheid van Onderskepping (LWO) d.m.v. lae senderdrywing, geënkodeerde golfvorms, wyebandfrekwensiegebruik, noue antennabundels en vermydende antennasoekpatrone. Hierdie eienskappe veroorsaak dat ’n LWO radar nie akkuraatheid en resolusie prysgee nie. Die term LWO verwys na die skrale kans of waarskynlikheid van onderskepping deur ’n ontvanger wat die radar se gedrag probeer naspeur. Die komplekse seinomgewing en vele grade van vryheid beskikbaar vir ’n LWO-radar, stel baie hoë eise aan onderskeppingsontvangers om gedetaileerde en akkurate inligting in reële tyd te lewer. Die ondersoek van LWO-radaronderskepping op sy eie is nie voldoende nie. Hierdie studie beskou die deteksie, parameter-estimasie asook klassifikasie (m.b.v. Fuzzy ARTMAP) van die Pilot Mk3 LWO-radar as ’n probleem in die geheel. Fuzzy ARTMAP is ’n kognitiewe neurale metode wat fuzzy-logika en Aanspasbare Resonante Teorie (ART) kombineer om kategorieë of klassifikasieprototipes te vorm en hulle te klassifiseer. Fuzzy ARTMAP stelsels bestaan uit selfvormende neurale komponente wat diskrete asook kontinue insette vinnig kan leer en klassifiseer. Om die geskiktheid van enige onderskeppingsontvanger te bepaal word ’n LWO-werkverrigtingsyfer gedefinieer. Hierdie werkverrigtingsyfer kombineer beide radar- en onderskeppings ontvanger vergelykings vir operasionele reikafstand en sensitiwiteit. Die radar beoog om die onderskeppingsontvanger tot binne sy eie reikafstand in te forseer om die ontvangerplatform op te spoor. Die onderskeppingsontvanger wil daarenteen op ’n veilige afstand (verder as die radarbereik) bly, en nogsteeds die radar se uitsendings onderskep. ’n Maksimale Waarskynlikheid (MW) detektor is ontwikkel wat die Pilot Mk3- radargolfvorms kan opspoor, met voldoende integrasie-aanwins vir betroubare deteksie en wat veel verder strek as die radarreikafstand. Akkurate radarparameterestimasie is ’n baie belangrike funksie in ’n onderskeppingsontvanger aangesien dit ’n direkte implikasie het vir die akkuraatheid van die klassifikasiefunksie. Vanuit ’n wye verskeidenheid van relevante radar parameters word estimasies van antennadraaitempo, senderfrekwensie, frekwensieveegbandwydte en veegherhalingstempo gebruik om die Pilot Mk3-radar te klassifiseer. Die estimasie van hierdie parameters is duidelik gegroepeer met geen oorvleuling om moontlike verwarring te voorkom. Indien die detektor deteksies verklaar, volg die estimasiefunksie met baie akkurate waardes van radarparameters. Die Fuzzy ARTMAP-klassifiseerder wat ontwikkel is vir hierdie studie beskik oor die vermoë om die Pilot Mk3 LWO-radar te klassifiseer. Korrekte Klassifikasiebesluite (KKB) van 100% is moontlik vir ’n verskeidenheid klassifiseerderverstellings. Die klassifiseerder behaal ’n goeie veralgemening van in- en uitset ruimtes, en die leer- (of oefen-) roetines is baie effektief met so min as 5% van die volle datastel. Enige radarstelsel wat roem op LWO moet sowel die radar as ’n moontlike onderskeppingsontvanger in gelyke maat beskou. Die LWO- werkverrigtingsyfer verskaf ’n handige maatstaf vir sulke evaluasies. Om bloot te eis dat ’n radar LWO-eienskappe teenoor enige onderskeppingsontvanger het, is te algemeen en nie insiggewend nie. Dieselfde geld vir ’n onderskeppingsontvanger wat 100% (of totale) onderskepping kan verrig teenoor enige radar. CopyrightDissertation (MEng)--University of Pretoria, 2012.Electrical, Electronic and Computer Engineeringunrestricte