Research On Intelligent Optimization Technology Of Smart Noise Suppression Jamming Waveform

With the continuous improvement of the intelligent level of modern radar,electronic warfare is also developing towards the field of "cognition".The current research on cognitive electronic warfare involves many aspects,but the research on the realization of intelligent optimization of jamming waveforms is still insufficient.In this context,taking smart noise suppression jamming as an example,this paper makes some exploration and research on the intelligent optimization technology of jamming waveform.Firstly,two generalized smart noise jamming patterns are studied,and their jamming principles and jamming parameters are deduced and analyzed.Interrupted-sampling and phase-modulation jamming combines Interrupted sampling and periodic repeater and pseudo-random sequence phase modulation.The spread spectrum characteristics of the latter can generate noise peaks covering a certain range near the false target group generated by the former.Frequency-phase mixed modulation jamming combines step-wave frequency modulation and cosine phase modulation,and the latter can expand the false targets with equally spaced distribution that the former can generate.By controlling the waveform parameters,the jamming effect formed by the two jamming patterns can be flexibly controlled,and the densely distributed false targets can achieve the effect of suppression jamming.Secondly,an intelligent optimization model of jamming waveforms based on virtual cognitive loops is constructed by using intelligent optimization algorithms.A waveform optimization module is added to the traditional jamming equipment.This module can simulate and construct the radar matched filter based on the intercepted radar signal and estimate the constant false alarm threshold,so as to evaluate the performance of different jamming waveforms.The intelligent optimization algorithm is used to overcome the problem that traditional optimization methods are difficult to realize the jamming waveform optimization,and realize the intelligent optimization effect of the interference waveform in different scenes.The results of iterative optimization are used to supplement and update the database to guide the synthesis of actual jamming waveform.Finally,a series of simulation experiments and analysis of jamming waveform optimization are carried out with interrupted-sampling and phase-modulation jamming and frequency-phase mixed modulation jamming.Experiments are carried out using genetic algorithm.Under the condition of self-defense jamming,the interrupted-sampling and phase-modulation jamming waveform is optimized,and the feasibility and optimization performance under different optimization space,different radar signals and changing radar signals are verified.Under the condition of shielding jamming,the frequency-phase mixed modulation jamming waveform is optimized,and the optimization performance under the condition of delay superposition and periodic repeated is verified.Experimental results show that the proposed intelligent optimization method of jamming waveform based on intelligent optimization algorithm is feasible,but the optimization performance under certain conditions needs to be improved.Thus,the particle swarm optimization algorithm is used to carry out simulation experiments of frequency-phase mixed modulation jamming waveform optimization.The performance of the two algorithms is preliminarily compared,and the influence of parameter setting of the two algorithms on their optimization performance is preliminary explored and analyzed,which laid a foundation for the subsequent algorithm improvement and performance improvement.