Research On The Intelligent Sensing Method Of Radar Main Lobe Active Jamming

In modern electronic warfare,the radar main lobe active interference enters the radar receiver from the main lobe together with the target echo,which causes the radar to fail to detect the target correctly,becoming one of the main threats to the radar system.In order to ensure the radar's working efficiency,the intelligent sensing of the radar main lobe active interference has become the focus of research.Radar main lobe active interference is mainly divided into radar active suppression interference and radar active deception interference.Radar active suppression interference achieves the effect of covering radar signals through high-power noise;radar active deception interference Modulation forwarding confuses the information of the real target.In order to ensure that the radar can complete the task adaptively and efficiently in the new electromagnetic environment,the research of the radar main lobe active interference intelligent sensing method has very important value and significance.This thesis focuses on the radar main lobe active interference intelligent sensing method.The main contents include:Firstly,aiming at the radar main lobe active suppression interference and deception interference,a mathematical model of interference is established,and the characteristics of the interference are studied.For radar active suppression interference,characteristic parameters in time and frequency domain with good separability are extracted.Based on the characteristics of radar active deception jamming,its short-time Fourier transform spectrum is used as a feature,which lays the foundation for the subsequent research on jamming intelligent sensing methods.Secondly,aiming at radar active suppression interference,intelligent sensing algorithms based on radial basis function neural network and back propagation neural network are proposed.The influence of structural parameters of radial basis function neural network and back-propagation neural network on recognition performance is studied.Four kinds of time-domain and frequency-domain characteristic parameters of radar active suppression interference are used to carry out simulation experiments.Optimal network parameter combination to suppress interference.Thirdly,based on the structure of the radial basis function neural network and the back propagation neural network with the optimal parameter combination,the effectiveness of the intelligent sensing algorithm for radar active suppression interference is experimentally analyzed,and five kinds of active suppression interference are given through simulation.The recognition probabilities at various signal-to-signal ratios are analyzed and compared for the recognition performance of these two neural networks,and the performance difference between the radar active suppression interference intelligent sensing algorithm proposed in this thesis and the previous recognition algorithms is compared.Fourthly,aiming at radar active deception jamming,intelligent perception algorithms based on deep belief networks and convolutional neural networks are proposed.Study the influence of the structural parameters of deep belief neural network and convolutional neural network on recognition performance,and use the short-time Fourier transform spectrum characteristics of radar active deception jamming for experimental simulation.Combining recognition probability and training time,the optimal network structure suitable for radar active deception jamming is selected.Finally,under the structure of the deep belief neural network and convolutional neural network with the optimal parameter combination,the effectiveness of the radar active deception jamming intelligent sensing algorithm is analyzed experimentally.The target echo and nine kinds of deception jamming are given by simulation.The recognition probability under the interference-to-noise ratio is analyzed and compared.The performance of these two neural networks is compared,and the performance difference between the intelligent radar active deception jamming algorithm proposed in this thesis and the traditional recognition algorithm is compared.