Research On Radar Plot Processing Under Complex Conditions

Radar plot processing is an important part in the radar system.It further processes the plot information obtained by echo signal processing,which lays the foundation for the next target tracking work.The radar working environment is increasingly complex.The radar system must not only face the complex background environment and the influence of various interferences,but meet higher detection accuracy requirements.In a complex environment,plenty of plots including targets,clutter,interference,and false alarms obtained through signal processing bring serious difficulties to the radar system's subsequent track processing,including system performance degradation and a large number of false track generations.This thesis focuses on the research of radar plot processing in complex environments,focusing on classification method based on multi-dimensional features to distinguish true and false plots,the selection of plot features,analysis and verification of filtering effects,preprocessing of plot data,and the method of filtering the false plots after the work of track start.The main contents of this thesis are summarized as follows:1.Firstly,the method of acquiring the original plots is introduced.The thesis briefly described the conventional radar signal processing flow,analyzed the reason for the formation of the remaining plots,and introduced plot clotting algorithm.Then plots are generated by simulation.The plot data and its clotting process lay the foundation for the next study on the characteristics of the plots and the classification of the plots.2.Aiming at the problem of an excessive number of plots left in complex environment,a method of using support vector machine to classify and filter plots based on extracting effective features is proposed to decrease the remaining false plots.Through analysis of remaining plot data,the difference between the characteristics of true and false plots is found,and the focus is on finding and using the characteristics of plot information from multiple ways to achieve real target plots and false target plots.The features proposed and adopted in this thesis mainly include plot compression number,plot diffusion degree,amplitude variance of the plot group,petal method characteristics,number of velocity matching plots,etc.The results of simulation experiments show that the combined use of multiple plot features including petal method characteristics,number of velocity matching plots,and amplitude variance of the plot group to classify and filter the plots result in more satisfactory performance,proving the benefit of classifying plots by using the multi-feature combination and the correctness of the plot features and the plot filtering method proposed in this thesis.3.In order to resolve the time-consuming problem of calculating plot characteristics and the problem that the remaining plots still cause the start of false tracks,the thesis proposes to use the density clustering method to preprocess the plot data,and then combine the logical method of track start and the decision tree to accomplish the track start work after plot filtering work.First,in consideration of the difference between the clutter plot density and the real plot density,the density clustering method is used to cluster several adjacent periodic plots in turn,so as to persist most of the real plots while removing some false plots.After preprocessing,the remaining plot data is classified and judged,and the track start based on the combination of logic method and decision tree is performed on the remaining plots judged as the real target plots,which further improves the accuracy of track start.The results of simulation analysis and measured data processing show that the method proposed in this thesis has a higher correct rate of track initiation than the method that directly uses the true and false plot identification and then starts the track.