Classifying low probability of intercept radar using fuzzy artmap

Abstract

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. Copyright