Waveform Optimization Design Of Intermittent Sampling And Forwarding Interference

In radar confrontation,radar and jammer check and balance each other and develop each other.On the other hand,the continuous development of radar technology also forces the continuous development of jammer technology.Among them,the development of cognitive radar also forces electronic countermeasures to develop in the direction of intelligence.Intelligent jamming waveform design is a method that requires electronic countermeasures equipment to continuously change the emission waveform according to the real-time changes of the external environment.Traditional jammers often transmit one or several fixed waveforms,which cannot adapt to changes in the signal environment,greatly reducing the combat effect of electronic jamming.In addition,in electronic warfare,the radar and jammer are independent equipment,which will occupy a lot of resources and have strong electromagnetic interference with each other,which will seriously affect the combat capability.Therefore,the future system will show a trend of one machine with multiple functions.The focus is to solve the problem of signal sharing.Based on the above questions,the specific research contents are as follows:Aiming at the problem that the traditional jamming algorithm can not adapt to the change of signal environment,the q-learning algorithm of reinforcement Learning algorithm and cutting false method are applied to the jamming waveform design,so that the jamming waveform can adapt to the length of radar signal.Link to interfere with the algorithm for radar detection,using constant false-alarm probability as environment interaction model,establish interference performance evaluation function,which interfere with the letter after the pulse pressure amplitude ratio of the mean and standard deviation,through reinforcement learning adaptive adjustment of intermittent sampling signal sampling and forward time,at the same time to cut the length of the unknown radar signal to achieve the goal of optimal interference.Finally,the ant colony algorithm and genetic algorithm are analyzed as comparative algorithms.The simulation shows that the ant colony algorithm,genetic algorithm and Q-learning algorithm are suitable for radar signal interference of known length.The final convergence value of Q-Learning algorithm is better than other algorithms,and the convergence effect of ant colony algorithm and genetic algorithm is not different.Q-learning algorithm combined with cutting dummy method is suitable for jamming unknown length radar signal and has real-time jamming effect.For jammer device has the function of launch,consider a detectable signal hidden in the interference signal,the launch of the jamming signal has the detection effect,this paper proposes a detecting interference based on non-uniform intermittent sampling repeat forwarding integration share the signal waveform,and the waveform is solving problems to the optimal solution of the coding sequence,Finally,deep Q learning algorithm is used to solve integrated waveform.In this method,the integrated signal model is firstly established,and the detection performance evaluation function is added from the perspective of fuzzy function on the basis of interference,that is,the joint maximization of the ratio of mean amplitude to standard deviation after distance,velocity resolution and pulse pressure of interference signal.Simulation results show that when the number of coding states is small,the convergence effect of deep Q learning algorithm and reinforcement learning algorithm is almost the same,and the convergence of deep Q learning algorithm is faster than that of genetic algorithm.When the number of coding states is large,the convergence speed of deep Q Learning algorithm is improved compared with genetic algorithm and Q-learning algorithm,and the optimal solution is more stable.