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Study On The Methods Of Radar Active Deception Jamming Integrated Sensing

Posted on:2014-08-27Degree:DoctorType:Dissertation
Country:ChinaCandidate:X TianFull Text:PDF
GTID:1268330425968622Subject:Signal and Information Processing
Abstract/Summary:PDF Full Text Request
With the generation and applicationof the digital radio frequency memory (DRFM)technology, the synthetical electronic counter measure (ECM) is one of the so-called“four threats” to the radar.The deception jammer consists of the DRFM, which cangenerate multiple modes, fleasibility and stronger opposability jamming. It forms themost severe threat to military radar. Moreover, although the existing radars are equippedwith some electronic counter-countermeasures (ECCM), they cannot choose theoptimum anti-jamming measurement automatically according to the jammingenvironment, as a result of the absence of the jamming mode sensing function.In order to enhance the radar early-warning and detecting performance in thecomplicated electromagnetic environment, the generation mechanism of jamming andthe mechanism of jamming on radar are studied. On the bases, the sensing methods ofactive deception jamming are researched, which can provid the priori information forthe subsequent radar deception jamming suppression. Then the radar will choose thebest ECCM methods against the decptive jamming.First, the generation mechanism ofthe deception jamming is analyzes detailedly.Then the deception jamming integratedsensing algorithms are studied from the signal processing, information processing,waveform diversity and fingerprint characteristics of the deceptive jammer. The maincontents and results are listed as follows.(1) The research background is ascertained, and the conventional ECCM isintroduced. The domestic and overseas research status of the radar active deceptionjamming sensing methods is addressed detailedly. Firstly, the generation mechanism ofthe radar deception jamming based on DRFM is introduced, and then the generationmechanism of the pull jamming based on the process is presented, including range gatepull jamming, velocity gate pull jammingand range-velocity gate pull jamming. Finally,other type of deception jamming is studied. The deception jamming model is thefoundation forthe further jamming sensing algorithms.(2) The typical gate pull deception jamming signal model of the tracking radar isestablished in the releasing process of the jamming, multi-scale decomposition theory isused to study the sensing algorithm of deception jamming. Two types of multi-scaledecomposition methods of deception jamming sensing algorithms are proposed, including those based on wavelet decomposition algorithm and an algorithm based onempirical mode decomposition. In the first algorithm, wavelet decomposition of thenormalized radar received signal is used to get the high-frequency detail componentsand the low-frequency approximation components. Normalized power ratio is extractedas the multi-scale decomposition coefficients of the radar received signal. Base on thoseabove, the identification algorithm of the three towing interference is achieved. In thesecond algorithm, the processing method is similar to the former. The intrinsic modefunction (IMF) components are derived directly from the empirical modedecomposition. The features of the decomposed components are extracted to achieve thesensing algorithm of three types of jamming.(3) Non-negative matrix factorization (NMF) vector characteristics of productspectrum and texture characteristic parameters of two-dimensional spectrum areextracted by joint frequency domain and slow-time domain processing. Based on these,identification algorithms of pull jamming are proposed. In the first algorithm, productspectrum matrix (PSM) of the frequency domain and slow-time domain is established atthe beginning. Then, NMF theory is applied to the PSM, and features are extracted fromeach vectors of the decomposed matrx. Finally, a deception jamming sensing algorithmis proposed. In the second algorithm, the texture features of image processing areintroduced into deception jamming sensing algorithm. The gray image texturecharacteristics of two-dimensional spectrum are extracted. The sensing algorithm ofdeception jamming is presented using texture parameters.(4) Detection algorithms of the presence of deception jamming based oninformation domain processing are proposed, which are two kinds of deceptionjamming sensing algorithms based on amplitude fluctuation features perception. Thedetection algorithms are the goodness of fit (GOF) and particle filter (PF), respectively.In the first algorithm, when the deceptive jamming appears, the amplitude fluctuation ofthe received signal within the radar beam is different from echo. The existence ofdeception jamming detection is established, and two types of GOF detection algorithmsare proposed. Finally, the performance of Anderson-Darling (AD) detection andmodified Anderson-Darling (MAD) detection is compared by the simulationexperiments under different conditions. In the second algorithm, the echo and jammingwithin the same beam is unresolved in the pull jamming capture period. Consideringthat the energy of received signal will spill over to adjacent matched filter sampling points, the signal model is established. Base on those above, a likelihood ratio testmodel is developed. Finally, the detection algorithms of the presence of pull jammingare implemented, which uses particle filterdetector.(5) The sensing algorithms of deception jamming based on the waveform diversitytheory is studied, including those of random linear frequency modulate ratio signal(RLFMR) and chaotic phase modulation (CPM) signal. For linear frequency modulate(LFM) signal and the phase-coded signal, the generalized likelihood ratio test (GLRT)algorithm and Holder coefficient feature extraction method are presented, respectively.The detection and identification performance for deceptive jamming is verified bynumerical simulation.(6) Based on the second modulation of jammers in the deception jamminggeneration process, the nonlinear distortion model of jammer is established usingVolterra model. The fingerprint feature of jammer is extracted. The identificationmethod of deception jamming is studied based on subspace and particle swarmoptimization (PSO) in sparse decomposition method.
Keywords/Search Tags:digital radio frequency memory (DRFM), active deception jamming, electronic counter-countermeasures (ECCM), deception jamming sensing, feature extraction
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