Study On Intelligent Jamming Policy And Jamming Methods Of SAR

Synthetic Aperture Radar(SAR)is becoming more and more powerful,and the SAR's countermeasure technology is constantly improving.How to counter SAR in real time in a complex battlefield environment,select better jamming methods intelligently,and use artificial intelligence technology to achieve intelligence jamming policy are becoming increasingly important.In this dissertation,the operating principle and imaging algorithm of airborne SAR are analyzed firstly.Based on the above analysis,the typical jamming methods of SAR are classified to study and a jamming method for airborne SAR is proposed.Finally,the intelligent jamming policy of SAR in imaging and non-imaging mode is studied.Missile SAR has non-imaging mode.The main research contents and achievements are as follows:1.A classification study on typical jamming methods of airborne SAR imaging is done.The methods can be classified to non-coherent noise jamming,partially coherent jamming and coherent jamming.For the non-coherent noise jamming,Radio frequency noise jamming,noise amplitude modulation jamming,noise frequency modulation jamming and noise phase modulation jamming are researched.For the partially coherent jamming,two-dimension mismatch jamming,repeater jamming and distance frequency shift decoherence jamming are researched.For the coherent jamming,two-dimension noise convolution jamming is researched.The simulation results verify methods' efficiency.2.For the coherent jamming,inspired by the idea that airborne SAR is affected by airflow fluctuations during motion which results in motion error,the motion error phase modulation jamming method is proposed,including ground velocity error phase modulation jamming and swing error phase modulation jamming.Theoretical analysis and simulation results show that the proposed method can effectively counter the phase gradient autofocus(PGA)algorithm,resulting in energy expansion in the azimuth direction and image defocusing,unable to imaging correctly.3.For the problem of SAR intelligent jamming policy,the jamming power of SAR in imaging and non-imaging mode is analyzed,and the intelligent selection network model of the jamming methods is simulated to realize.For the imaging mode,select ten barrage jamming methods as jamming resource library firstly.Then,select signal carrier frequency,pulse width,bandwidth,pulse repetition frequency PRF and central slant range as input parameters of the network.The SAR image evaluation indicators are used to evaluate the jamming effects and the best jamming method is selected as the network output parameter.Finally,train the deep neural network to realize the intelligent selection of the jamming methods.For the non-imaging mode of missile SAR,firstly,the effect evaluation of the barrage jamming methods and deception jamming methods in search,tracking and combat modes are made,respectively.The effect of the barrage jamming methods is evaluated by the detection probability and the effect of the deception jamming methods is evaluated by the false alarm probability to select the best jamming methods in these modes.Then,select pulse repetition frequency PRF,pulse width and bandwidth as input parameters,the best jamming method as output parameter of the network.Finally,train the BP neural network to realize the intelligent selection of the barrage jamming methods and the deception jamming methods.