Extraction And Identification Of Maritime Channel Features

With the continuous development of communication technology,wireless security issues such as eavesdropping and forging information content with wireless signals as the carrier are facing greater challenges.Based on the randomness,spatio-temporal uniqueness,short-term stationaryness and unpredictability of the wireless channel,dynamic keys can be generated by using the characteristics of the physical layer channel,which realizes the perfect security of approximating "one secret at a time",which is of great significance for communication security.Through the study of information security theory and the current research status at home and abroad,it is found that accurate measurement and estimation of channel characteristics is the basis.This puts forward higher requirements for the accuracy and complexity of the identification and extraction of channel features.The research team built a offshore wireless channel measurement platform in the offshore environment near Lingshui Bay in Dalian,and used the spectral correlation method of Chlip signal as a multipath measurement tool for actual measurement,and the results showed that the multipath transmission environment of the offshore wireless channel was reproducible in a limited time,and the repeated measurement data had correlation,which could be used for the identification and extraction of channel multipath information,but due to the influence of noise and side lobes,it was difficult to identify multipath identification,especially the identification of multipath weak signals.The chirp signal matches the side lobes of the filtered waveform to overwhelm the higher-order multipath main lobes,interfering with the accuracy of the estimation and recognition of channel state information.In order to solve this problem,this project uses a nonlinear weighted filtering algorithm to perform sidelobe suppression,and the basic principles and defects of the SVA algorithm are studied.The SVA algorithm takes the corresponding weighted function parameters of the main lobe and the side lobe to achieve the purpose of removing the side lobe while basically maintaining the width of the main lobe.The defects of the SVA algorithm were improved,and the improved algorithms were compared through three evaluation indicators.Experimental results show the effectiveness of the improved SVA algorithm on the suppression of the side lobe.In order to solve the problem of multipath aliasing so that high-order multipaths are flooded by the main lobe of low-order multipaths,this project uses the CLEAN algorithm based on least squares method to iterate on the flooding problem of real multipath channel signals.Among them,the CLEAN algorithm can adaptively extract multipath weak signals for multipath aliased channel signals.However,with the increase of alias multipath and the increase of attenuation,the error of the traditional CLEAN algorithm will gradually increase,in order to eliminate the delay,the estimation error of the amplitude value parameter,and the subsequent cumulative error,this study uses the least squares method to estimate the parameter,thereby reducing the error.Finally,using the multipath transmission environment of the offshore wireless channel in a limited time range with reproducibility characteristics,multipath clusters with similar propagation characteristics are obtained by repeated measurements many times.Considering the correlation of multipath information and the uncorrelation of noise in the view data,this study proposes a multipath cluster clustering recognition algorithm for offshore wireless channels based on density peak clustering(DPC)algorithm to improve the recognition ability of weak multipath signals.