Research On Feature Extraction And Recognition Methods For Interrupted Sampling And Forwarding Jamming

Today,with the rapid development of radar active deception jamming technology,the interrupted sampling and forwarding jamming has been widely used in the battlefield,such as the Russian-Uzbekistan war in the past two years.The interrupted sampling and forwarding jamming has played a vital role in the battlefield.The interrupted sampling and forwarding jamming has the effect of multiple false targets deception,and is a good means of electronic countermeasures,In order to enable the radar system to detect the real target normally in this case,it is necessary to add the jamming recognition function in the radar system,so that it can correctly identify the true and false targets when receiving the false target jamming,so that the radar can have excellent anti-jamming capability,so that the combat side can take the initiative in the battlefield and gain the first chance.Therefore,in order to better realize the recognition of jamming,it is necessary to understand the principle,implementation and type of interrupted sampling and forwarding jamming,and provide a basis for jamming recognition.This article focuses on the research of recognition methods for interrupted sampling and forwarding jamming.A signal feature extraction method based on integral bispectrum and a one-dimensional residual neural network based on Efficient Channel Attention(ECA)are proposed,and a large number of simulations have been conducted to verify the effectiveness of the feature extraction method.The main work is as follows:Firstly,the significance and background of interrupted sampling and forwarding jamming recognition are elaborated,and the current development status at home and abroad in this direction is introduced and summarized in detail.Provide a detailed introduction to active jamming based on Digital Radio Frequency Memory(DRFM)technology,and then conduct research on the principle of interrupted sampling and forwarding jamming based on DRFM.Research the principle of interrupted sampling and forwarding jamming generation based on,construct a model of interrupted sampling and forwarding jamming,and finally carry out engineering implementation based on FPGA hardware platform.Secondly,the feature extraction method based on integral bispectrum is introduced for the radar received signal under interrupted sampling and repeater jamming.This method calculates the integral bispectrum of the radar received signal after pulse compression processing,and classifies and identifies different target signals by using the difference of the integral spectrum curve between different signals.Thirdly,for the intermittently sampled and forwarded jamming,the target signal and jamming signal are extracted by the integrated bispectrum feature.Taking the interrupted sampling and repetition-forwarding jamming as the representative of the research object,the integrated bispectrum feature extraction is carried out for the jamming signal under different jamming parameters,and the integrated bispectrum feature extraction is carried out for the real target echo.The integration curves of the two are compared to reflect the feature difference and form a data set.Finally,the method of machine learning or deep learning is used to process and classify the data set to achieve the classification and recognition of jamming signals and target signals.By comparing the various indicators of classification and recognition,the classification and recognition of interrupted sampling and forwarding jamming is realized.