Broadband Fusion of Multiband Radar Signals Based on Optimal Dictionary Selection
Multiband Fusion is an effective way to broaden bandwidth of radar,which plays a key role in the detection and recognition of small-scale target.However,the existing multiband fusion algorithms still face the problems of slow operation and low precision.Therefore,a super-resolution technique of multiband fusion based on optimal dictionary selection and orthogonal matching pursuit is proposed in this paper.Firstly,the parametric model of multiband radar signal is conducted.Next,Snake Optimizer(SO)is applied to the coherent processing.Then,an Orthogonal Matching Pursuit(OMP)algorithm based on the optimal Geometrical Theory of Diffraction(GTD)dictionary selection is used to extrapolate the vacant spectrum.Experiment results of simulated and measured data are given.Experimental results show that the proposed method can effectively achieve super-resolution.This method combines simplified model rough estimation with complete model fine estimation,effectively reducing the amount of computation and realizing fast and accurate multiband fusion extrapolation processing.
万方数据
维普
