Cognitive Signal Processing And Clutter Suppression Technology Of Airborne Array Radar

The new airborne radar introduces the working mode of cognitive radar,has the ability to perceive environmental information,the static clutter characteristic database provides a priori information,intelligent sample screening provides usable samples that meet the RMB criteria,and space-time adaptation Processing based on knowledge-aided(KA-STAP)technology constructs knowledge-aided filters,completes the target detection of environment matching,and forms a closed-loop radar system with a feedback mechanism and intelligent information processing.In this thesis,geographic information system(GIS)and digital elevation model(DEM)are used to establish a knowledge base of clutter characteristics and carry out modeling of highly realistic environmental clutter.Cognitive space-time adaptive processing is applied to improve STAP's clutter suppression and target detection capabilities in a non-uniform clutter environment.The thesis focus of this thesis is as follows:(1)Research on the modeling method of airborne radar clutter based on geographic informationAiming at the problem of how to apply a priori geographic information to airborne radar.Use the geographic information system(GIS)to classify the terrain,and build a backscattering coefficient model based on the empirical model of the ground cover types and clutter characteristics obtained by the MIT Lincoln Laboratory;according to the actual terrain given by the digital elevation model(DEM)Elevation data to establish an airborne radar wiping angle model.A method of matching radar coordinate data with a priori clutter database is proposed,and high-fidelity environmental clutter modeling based on geographic information is completed.(2)Research on intelligent sample selection algorithm for airborne radar space-time adaptive processingAiming at the problem of how to select available samples in a non-uniform clutter environment.In the space-time adaptive processing of airborne radar,whether the samples used for clutter covariance matrix estimation follow independent and identical distribution is crucial to the design of space-time filters.Two types of intelligent sample screening algorithms are studied: adaptive sample screening and terrain attribute-based sample screening,where adaptive sample screening uses a generalized inner product algorithm and a sample screening algorithm based on spectral similarity.By comparing the generalized inner product value,Fourier spectrum and terrain attributes of the cell under test(CUT)and the sample to be screened,a training sample similar to the CUT is obtained to prepare for the accurate estimation of the clutter covariance matrix.(3)Research on knowledge-aided space-time adaptive processing technologyAiming at the problem of insufficient STAP performance caused by insufficient samples in a non-uniform clutter environment.Knowledge-aided space-time adaptive processing(KA-STAP)technology uses a priori information to construct the priori covariance matrix of the CUT,and the color-loading technique is used to obtain the colorloading matrix of the weighted sum of the priori covariance matrix and the sample covariance matrix.The pre-whitening(PW)method optimizes the color loading coefficients to obtain the optimal weight of the knowledge-aided filter.KA-STAP technology effectively improves the clutter suppression performance of airborne radar in non-uniform clutter scenarios.