Clutter Suppression For Slow Moving Target Detection Based On Subspace Method

The target detection technology using Radar has a wide application in Unmanned Aerial Vehicle(UAV)detection,intelligent driving and other fields.However,when detecting such target as UAV and ground pedestrians,on the one hand,the actual detection environment is complex,and the targets to be detected are often affected by strong background clutter.On the other hand,the moving targets are slow and the radar cross section(RCS)of the targets is small,which results in the weak echo energy and easy to be submerged in the strong clutter,making it difficult to carry out effective detection.Therefore,it is of great significance to suppress the clutter effectively so as to improve the detection performance of slow-moving targets in the strong clutter environment.This paper focuses on the problem of slow-moving target detection in the strong clutter environment,the clutter suppression algorithm based on subspace method is discussed,and the clutter basis selection and the subspace projection on the transform domain are studied respectively.The content of this paper includes the following aspects:1.First of all,the thesis mainly introduces the research background and significance of the detection of slow moving targets and the current research of the existing clutter suppression technology in strong clutter environment.The application and the existing problems in clutter suppression based on the subspace method are summarizes.Then,the echo model of moving target is introduced,the statistical characteristics of the ground clutter model and the method of clutter modeling are analyzed,and the simulation experiment is carried out.Finally,the basic principle,advantages and disadvantages of the traditional clutter suppression technology are expounded.The moving target indication(MTI)and moving target detection(MTD)are simulated and analyzed.2.To solve the problem that select the clutter basis accurately in the subspace method,the clutter suppression method based on K-means clustering and Singular Value Decomposition(SVD)subspace projection is proposed.The distribution of singular value spectrum,the features of spatial correlation and mean Doppler frequency from the singular vector can be obtained by SVD of the echo signal.Based on these,the singular components are classified by the K-mean clustering algorithm.It can directly estimate the cluster Eigen Rank without preset threshold on parameters,and can accurately find the singular vector corresponding to the clutter subspace.Thus,the clutter component in the echo signal is suppressed by subspace projection.Finally,the experimental results show that the proposed detection has obviously better performance on slow moving targets under low signal-to-clutter ratio environment.3.Aiming at the difficulty of separating the target subspace from the clutter subspace,a subspace projection clutter suppression algorithm based on fractional Fourier transform is designed.Firstly,the phase coding in slow time dimension is carried out for the transmitted signal,and phase decoding is carried out at the receiving end to suppress the clutter.At the same time,the clutter has the characteristics of linear frequency modulation,and fractional Fourier transform gets the effect of energy aggregation on LFM signal,which makes the target and clutter have greater differentiation,thus creating favorable conditions for the selection of clutter base and subspace projection algorithm.