Research On Detection Method Of Constant False Alarm Of A Radar In Complex Background

Because the actual background environment of the radar is complex,the background environment is not a uniform background,but a complex environment containing targets,noise,clutter,interference,etc.How to extract target information from a complex background environment is particularly important.As a kind of adaptive threshold technology,the radar signal constant false alarm processing technology is widely used in the automatic detection of radar targets.After years of development and research,the theory of Constant False Alarm Rate(CFAR)detection technology has achieved a lot of results,but most of the constant false alarm detectors are difficult to achieve performance balance in different background environments,especially facing the increasing false alarm probability in the clutter edge environment and significant degradation in detection performance in a multi-target environment.Based on the analysis of the existing constant false alarm detection technology,this thesis studies the radar constant false alarm detection technology in complex background.The main work of the thesis is as follows:1)Some basic theories and detection criteria commonly used in radar signal detection are discussed,which lays the foundation for subsequent research.The constant false alarm detection methods of mean class(ML)and ordered statistical class(OS)are analyzed,and their performances in uniform clutter background,multi-target clutter background and clutter edge background are simulated and compared.It provides a basis for the performance evaluation of the subsequent constant false alarm detector.2)The advantages and disadvantages of the variable exponential(VI-CFAR)constant false alarm detector are analyzed.The shortcomings of VI-CFAR are facing the increasing false alarm probability in the clutter edge environment and significant degradation in detection performance in a multi-target environment.At the same time,because of the excellent detection performance of adaptive censored method in multi-target background and combined with adaptive censored(ACMLD)method and mean value CFAR method,this paper puts forward several new improved methods.Based on the analysis and comparison of various improved methods,the best performance detection method is combined with VI-CFAR to form a new adaptive constant false alarm detection method.The simulation results show that the improved constant false alarm detection method can effectively control the false alarm probability under the clutter edge background,and have a good detection performance under the multi-target background and effectively solve the problem of VI-CFAR in non-uniform background.3)For the multi-target background environment,CA-CFAR detection methods are combined with the cyclic deletion to form a censored cell averaging(CCA-CFAR)detection method,which is based on the improvement and extension of the CA-CFAR detection method.The improved detection method does not require sorting processing of the distance unit,so the computational efficiency is more efficient than the ordered statistical class(OS-CFAR)method.This improved detection method is combined with VI-CFAR detector to form a new detection method.The simulation results show that the improved detection method has a good performance under the non-uniform background,which can effectively reduce the occurrence of target occlusion.Moreover,the improved detection method improves the detection performance with the increasing number of interference targets.The improved detection method is more inclusive in a multi-target environment.