Identification of Radar Signals Based on Time-Frequency Agility using Short-Time Fourier Transform

With modern advances in radar technologies and increased complexity in aerial battle, there is need for knowledge acquisition on the abilities and operating characteristics of intercepted hostile systems. The required knowledge obtained through advanced signal processing is necessary for either real time-warning or in order to determine Electronic Order of Battle (EOB) of these systems. An algorithm was therefore developed in this paper based on a joint Time-Frequency Distribution (TFD) in order to identify the time-frequency agility of radar signals based on its changing pulse characteristics. The joint TFD used in this paper was the square magnitude of the Short-Time Fourier Transform (STFT), where power and frequency obtained at instants of time from its Time-Frequency Representation (TFR) was used to estimate the time and frequency parameters of the radar signals respectively. Identification was thereafter done through classification of the signals using a rule-based classifier formed from the estimated time and frequency parameters. The signals considered in this paper were the simple pulsed, pulse repetition interval modulated, frequency hopping and the agile pulsed radar signals, which represent cases of various forms of agility associated with modern radar technologies. Classification accuracy was verified using the Monte Carlo simulation performed at various ranges of Signal-to-Noise Ratios (SNRs) in the presence of noise modelled by the Additive White Gaussian Noise (AWGN). Results obtained showed identification accuracy of 99% irrespective of the signal at a minimum SNR of 0dB where signal and noise power were the same. The obtained minimum SNR at this classification accuracy showed that the developed algorithm can be deployed practically in the electronic warfare field for accurate agility classification of airborne radar signals

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

Nonlinear Suppression of Range Ambiguity in Pulse Doppler Radar

Coherent pulse train processing is most commonly used in airborne pulse Doppler radar, achieving adequate transmitter/receiver isolation and excellent resolution properties while inherently inducing ambiguities in Doppler and range. First introduced by Palermo in 1962 using two conjugate LFM pulses, the primary nonlinear suppression objective involves reducing range ambiguity, given the waveform is nominally unambiguous in Doppler, by using interpulse and intrapulse coding (pulse compression) to discriminate received ambiguous pulse responses. By introducing a nonlinear operation on compressed (undesired) pulse responses within individual channels, ambiguous energy levels are reduced in channel outputs. This research expands the NLS concept using discrete coding and processing. A general theory is developed showing how NLS accomplishes ambiguity surface volume removal without requiring orthogonal coding. Useful NLS code sets are generated using combinatorial, simulated annealing optimization techniques - a general algorithm is developed to extended family size, code length, and number of phases (polyphase coding). An adaptive reserved code thresholding scheme is introduced to efficiently and effectively track the matched filter response of a target field over a wide dynamic range, such as normally experienced in airborne radar systems. An evaluation model for characterizing NLS clutter suppression performance is developed - NLS performance is characterized using measured clutter data with analysis indicating the proposed technique performs relatively well even when large clutter cells exist

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

The detection of unknown waveforms in ESM receivers: FFT-based real-time solutions

Radars and airborne electronic support measures (ESMs) systems are locked in a tactical battle to detect each other whilst remaining undetected. Traditionally, the ESM system has a range advantage. Low probability of intercept (LPI) waveform designers are, however, more heavily exploiting the matched filter radar advantage and hence degrading the range advantage. There have been literature and internal, SELEX Galileo proposals to regain some ESM processing gain of low probability of intercept (LPI) waveforms. This study, however, has sought digital signal processing (DSP) solutions which are: (1) computationally simple; (2) backward-compatible with existing SELEX Galileo digital receivers (DRxs) and (3) have low resource requirements. The two contributions are complementary and result in a detector which is suitable for detection of most radar waveforms. The first contribution is the application of spatially variant apodization (SVA) in a detection role. Compared to conventional window functions, SVA was found to be beneficial for the detection of sinusoidal radar waveforms as it surpassed the fixed window function detectors in all scenarios tested. The second contribution shows by simulation that simple spectral smoothing techniques improved DRx LPI detection capability to a level similar to more complicated non-parametric spectral estimators and far in excess of the conventional (modified) periodogram. The DSP algorithms were implemented using model-based design (MBD). The implication is that a detector with improved conventional and LPI waveform detection capability can be created from the intellectual property (IP). Estimates of the improvement in SELEX Galileo DRx system detection range are provided in the conclusion

Adaptive Coding, Modulation and Filtering of Radar Signals

In this chapter, some of the issues associated with radar signal processing are highlighted, with an emphasis on adaptability. Signal processing operations are carried by systems in order to enhance the received signal or to clarify its content of information. Received radar signal should be subjected to processing prior to the extraction of useful target information out of it so as to emphasize desired signal among other accompanying signals. Processing of the radio frequency (RF) signal is generally done in an analogue manner, while digital signal processing (DSP) became dominant in the intermediate-frequency (IF) and low-frequency portions of the system. Since the detectability and immunity against interference and clutter strongly depend on the waveform used, it will be more efficient to apply a diverse waveform instead of confinement to an invariable waveform of a fixed code and pattern. Adaptive coding, modulation and filtering of radar signals provide high degree of diversity as well as flexibility and agility for signal processors versus changing sources of interference and environmentally dependent reflectors. Constant false alarm rate (CFAR) is an adaptive processing technique that reduces noise and clutter. Different methods are applied in CFAR technique to adaptively cope with varying clutter density and distribution

Suppression approach to main-beam deceptive jamming in FDA-MIMO radar using nonhomogeneous sample detection

Suppressing the main-beam deceptive jamming in traditional radar systems is challenging. Furthermore, the observations corrupted by false targets generated by smart deceptive jammers, which are not independent and identically distributed because of the pseudo-random time delay. This in turn complicates the task of jamming suppression. In this paper, a new main-beam deceptive jamming suppression approach is proposed, using nonhomogeneous sample detection in the frequency diverse array-multiple-input and multiple-output radar with non-perfectly orthogonal waveforms. First, according to the time delay or range difference, the true and false targets are discriminated in the joint transmit-receive spatial frequency domain. Subsequently, due to the range mismatch, the false targets are suppressed through a transmit-receive 2-D matched filter. In particular, in order to obtain the jamming-plus-noise covariance matrix with high accuracy, a nonhomogeneous sample detection method is developed. Simulation results are provided to demonstrate the detection performance of the proposed approach

Time-frequency, bi-frequency detector analysis of noise technology radar

Enemy integrated air defense systems (IADS) using low probability of intercept (LPI) emitters can cause significant problems for suppression of enemy air defense (SEAD) techniques. New threat emitter configurations using low-power random noise modulation have a significant processing gain unavailable to non-cooperative intercept receivers. Consequently, the detection of these emitters can not be accomplished with conventional intercept receiver detection methods. This thesis examines the use of time-frequency, bi-frequency signal detection techniques to identify the parameters of the four types of continuous waveform noise radar recently reported. These include: (a) random noise, (b) noise plus frequency modulation continuous wave (FMCW), (c) noise FMCW plus sine and (d) random binary phase modulation. Quadrature mirror filtering for wavelet decomposition is used to investigate the four types of noise signals in order to extract the signal parameters. The FFT accumulation method for estimating the spectral correlation density function is also used to examine the cyclostationary bi-frequency properties of the waveforms. In addition, the periodic autocorrelation function and periodic ambiguity function are studied to determine the waveform properties in the delay- Doppler offset domain. Results show that non-cooperative intercept receivers can increase their processing gain using these types of signal processing techniques providing a more efficient response time to the threat.http://archive.org/details/timefrequencybif10945263