Research On Fast Searching Method Of Main Ridge Section Of Fuzzy Function

The deinterleaving of emitter signals is a central technology in the field of current electronic countermeasure.In modern war,the signal density in the electronic threat environment is getting higher and higher and working system of the radar system equipped by both sides is also becoming more and more advanced and complex,which makes signals in the actual electronic threat environment to have changed a lot.On the other hand,the conventional methods of pulse trains deinterleaving are generally based on five conventional parameters,i.e,time of arrival(TOA),radio frequency(RF),pulse width(PW),pulse amplitude(PA)and direction of arrival(DOA).But these methods are powerless to cope with today's bad signal environment.Consequently,studying some effective features that can sort radar emitter signals of new complex system to make up for the insufficiency of the five conventional parameters is an effective way to solve the current deinterleaving of emitter signals.For this reason,Dr.Pu Yun-Wei proposes new characters called Ambiguity Function Main Ridge Slice Features and applys these features to sorting radar emitter signals.It shows that,the features of AFMR slice have strong compactness within clusters and good performance of anti-noise.But,the search speed of the this method with exhaustive search strategy is still to be further improved as for AFMR Slice..Therefore,there are some more efficient intelligent search methods which can cope with this problem.In order to further improve the search efficiency of AFMR Slice,this paper studys some new intelligent optimization algorithms in recent years,and applys the standard Grey Wolf Optimization algorithm and the improved GWO to search the AFMR Slice and extract the features of AFMR Slice.Meanwhile,for comparing the performance of several existing intelligent methods,an evaluation method for evaluating the search performance of the intelligent search methods is proposed in this paper.In this method,the same weight is allocated for each intelligent method to make a comprehensive evaluation of each intelligent method in terms of search efficiency and search precision and anti noise ability.The main research work in this paper is as follows:(1)In order to further improve the search efficiency of AFMR Slice,this papermakes an in-depth study on standard GWO and finds that the standard GWO has the advantages of strong global search ability and good robustness,which is very suitable for the problem to be optimized in this paper.Then,the standard GWO is used to search the AFMR Slice of six typical emitter signals and extract the features of the AFMR Slice.The experimental results show that,the search efficiency is 71.3% higher than the exhaustive method and the precision of search is increased by 2.48% compared to the exhaustive method.In the environment of low SNR,it has high stability and 90% average separation success rate,and the anti noise performance is good.(2)On the basis of standard GWO,an improved GWO combining with a uniform initialization strategy and a new nonlinear convergence factor and an adaptive updating population strategy is proposed,to search the AFMR Slice of six typical emitter signals and extract the features of AFMR Slice in this paper.The experimental results show that,the search accuracy is further improved to 2.52% than the exhaustive method and the search efficiency was further increased by 6.5% on the basis of standard GWO.Improved GWO still maintains high stability and the average separation success rate is 92.3% so that its anti noise performance is better.(3)This paper has already studied four intelligent search methods(Genetic Algorithm with superiority heredity?improved Particle Swarm Optimization algorithm?standard GWO and improved GWO.In order to compare the search performance of the four intelligent search methods,an evaluation method for evaluating the search performance of the intelligent search methods is proposed.This method considers that the search efficiency and the search precision and the anti noise ability of the intelligent search methods are equally important,and the same weight should be allocated for it.In the end,the integrated weighted score is considered as a comprehensive evaluation of each intelligent search method.