Radar Super-resolution Imaging Method Of Moving Platform Based On The Regularization Theory

Super-resolution imaging method is the important way that can achieve forward-looking imaging for airborne radar.Many practical applications,such as the airplane autonomous landing,air-dropping supplies,terrain avoidance and land attack,etc,require that the airborne radar platform has the ability of forward-looking angular super-resolution imaging.The noise sensitivity caused by the non-full rank of measurement matrix and the influence of Doppler phase caused by platform moving may lead to the imaging error.Therefore,new methods should be explored to achieve forward-looking super-resolution imaging under the above two circumstances.To solve the problems of super-resolution imaging in the low SNR and the moving platform,this thesis analyzes the echo model of the radar forward-looking,builds the echo model of forward-looking imaging and focuses on the two methods which are sparse super-resolution method based on truncated singular value decomposition strategy and complex deconvolution method based on the regularization strategy.The main content is as follows:1.According to the radar forward-looking scanning process,the geometric relationship between the moving platform and the target is analyzed.The angular echo is constructed as the convolution of antenna pattern and target scattering coefficients,which is the basis of the super-resolution imaging methods.2.The construction form of the measurement matrix under different edge scenes is researched and the noise sensitivity of deconvolution process is analyzed by the singular value decomposition theory,which provides the idea for solving the problem.3.The sparse super-resolution method based on truncated singular value decomposition(TSVD)strategy is proposed.The performance of super-resolution imaging for strong target is improved and false targets are avoided.Based on the theory of TSVD,the factors causing noise sensitivity are removed.Based on the regularization strategy,the prior information of target is added to improve the resolution.The super-resolution imaging for sparse target can be achieved when the SNR is low.4.The complex deconvolution method based on the regularization strategy is proposed.The influence of Doppler phase caused by platform moving on the deconvolution process is analyzed.The expression of velocity boundary is deduced.The high and low speed reference boundary is given.The complex measurement matrix is constructed by antenna pattern and Doppler phase.The deconvolution is realized by using amplitude and phase information.The super-resolution imaging for fast moving platform is achieved by the proposed method.The effectives of the above models and methods are demonstrated by the simulations and experimental data.Through the above two methods,forward-looking super-resolution imaging can be achieved under the low SNR and fast moving circumstances.