Deception Jamming Identification Of Multistatic Radar System Based On Model

Electronic warfare is an important part of modern warfare,and radar confrontation is a crucial link in electronic warfare.Deception jamming in radar confrontation is an important type of interference:enemy jammers cause deceptive interference to our radar by forwarding radar echoes in electromagnetic space,which makes our radar mistaken that there are a large number of targets in space.The purpose is to hide the real target.In order to combat the deception jamming,this dissertation models the deception jamming process as a Hammerstein-Wiener model,quantizes the radar receiving echoes,maps them into the model parameter space,and classifies them,while using the multi-angle observation and information sharing of the networked radar.Advantages,the information of each radar in the network radar is combined to identify and detect deceptive jamming,and the original method is improved.The main research work of this dissertation is as follows:1.The process of deception jamming is modeled as Hammerstein-Wiener model,and the inertia weight particle swarm algorithm,adaptive niche genetic algorithm and neural network algorithm commonly used in model parameter identification are introduced.The new model identification method proposed later is proposed.Provide theoretical support.2.For the above-mentioned intelligent algorithm,the search time is slow and the accuracy of the solution is not high.Based on the basic neural network,the convolution layer is added,and the attention to the subtle changes of the model is increased.The convolution is introduced.The application of neural network algorithm in model identification.This algorithm is used to identify and detect fraudulent interference,and compare it with inertia weight particle swarm optimization algorithm,adaptive niche genetic algorithm and neural network algorithm.The simulation results show that the convolutional neural network algorithm has the characteristics of high precision and high speed compared with the intelligent algorithm and the basic neural network algorithm,which meets the requirements of accuracy and real-time in electronic warfare.3.The process of deceptive interference recognition under the networked radar is improved,and two multi-radar fusion methods are proposed.The information fusion is carried out from the feature layer and the decision layer respectively,and the single radar identification method is extended to multi-radar to improve the performance of the original method.The simulation results show that in the background of five radar fusions,the recognition rate of feature layer fusion algorithm is improved by 8.1%and the decision layer fusion algorithm is improved by 12.3%compared with the single radar deception jamming algorithm.The effectiveness of the fusion algorithm is proved.