Research And Hardware Implementation Of Sparse Target Reconstruction For Airborne Radar

In recent years,airborne radar plays an increasingly important role in national defense construction,and it has higher requirements for the resolution and real-time performance of super-resolution processing of airborne radar.Airborne forward-looking radar is widely used,but the resolution of the traditional forward-looking scanning area is low,and the calculation complexity of the method is high.Therefore,it is of great theoretical and practical significance to realize a high resolution and low computational complexity of airborne forward-looking radar.This dissertation studies a super resolution method of self correlation iterative reconstruction of airborne forward-looking radar.The following work is carried out for the problem that the new technology can not be processed in real time due to the large amount of data(1)The echo model of airborne radar is established and the distance history of the point target is deduced.The virtual array model of echo signal based on the scanning radar system and the iterative reconstruction super-resolution method of echo autocorrelation matrix are studied,and the algorithm simulation is carried out.(2)Based on Bayesian information criterion,sparse target reconstruction of echo matrix is carried out,and the resolution of imaging results is improved.(3)For the inverse part of the matrix which takes a lot of time in the super-resolution method of autocorrelation reconstruction,based on the Hermitian Toeplitz structure of the echo signal autocorrelation matrix,the Hermitian Levinson recursive method is used to improve the matrix inversion method,which reduces the calculation amount of the algorithm,reduces the multiplier of half the matrix inversion process,and uses FPGA to realize the advantages.(4)The signal processing board of radar system is designed and implemented based on FPGA chip.The design and test of these modules include digital down conversion,pulse compression and distance walk correction,and the fast autocorrelation iterative reconstruction super-resolution module based on Hermitian Levinson recursive.