Research On Sparse Signal Recovery Algorithm With Application To Radar Imaging

Radar image technology can obtain fine information about the target structure,which has great importance in both military and civilian application.In order to improve the ability of radar imaging,the sparse signal recovery algorithms is used to radar imaging by utilizing the sparse characteristics and structure characteristics of targets in imaging scene.The research problems including robust inverse synthetic aperture radar?ISAR?imaging in short coherent processing interval?CPI?,low sidelobe imaging of sparse array multiple input multiple output?MIMO?radar.The main work and contribution can be summarized are as follows.1.High resolution ISAR imaging algorithm based on sparse signal recoveryFor the problem of ISAR imaging in low signal noise ratio?SNR?,a Bayesian sparse recovery based on parameter iterative optimization is proposed.ISAR imaging is converted to sparse constraint maximum a posteriori estimation under Bayesian criterion,the target reconstruction and the noise power can be obtained simultaneously by exchanging iterative solution,then the target can be recovered.Compared with the conventional sparse recovery algorithms,the proposed algorithm can adjust parameters automatically and has superior robustness.A single loop structure smoothed l₀norm sparse signal recovery algorithm is proposed to obtain high resolution ISAR imaging.The imaging problem is mathematically converted into minimization l₀ norm optimization problem.A single loop structure is proposed to instead of the double loop layers in the SL0 algorithm to solve the minimization value of cost function,the cost function is undated.The algorithm can ensure the recovery performance and reduce computation time.2.ISAR imaging algorithm based on multi-dimensional sparse signal recoveryIn order to solve the problem of huge memory and the computational complexity load caused by vectorization processing to datas of ISAR imaging,sparse recovery ISAR imaging can be obtained by two dimensional?2D?smoothed ₀l norm to approximate ₀l norm of signals.The real data experiment show that the algorithm can improve computation efficiency.For three dimensional?3D?ISAR imaging of target,the 3D sparse representation of received echo is analysed,3D SL0 sparse signal recovery algorithm is used to ISAR imaging,the simulation experiment shows that the algorithm can reduce the complexity and computational load.3.ISAR imaging algorithm based on block sparse signal recoveryIn order to obtain fast high resolution ISAR image,the ISAR imaging is converted to the optimization of l_p?7?0?27?p?1?8?norm,the block sparse signal recovery algorithm of iterative weighted l_p norm is used and combined with the idea if signal's weight.The multiple measurement vectors?MMV?ISAR echo model is established,the MMV block smoothed ₀l norm sparse signal recovery ISAR imaging algorithm is proposed.The experiment results show that the algorithm can improve computation efficiency and imaging quality.4.MIMO radar imaging algorithm based on sparse signal recoveryIn order to solve high sidelobe problem of MIMO radar imaging of sparse array,the revised Newton SL₀ sparse signal recovery algorithm is used to MIMO radar imaging.An hyperbolic tangent function is proposed as the smoothed function to approach ₀l norm,the imaging can be converted to the optimization of ₀l norm,the revised Newton method is used to solve the optimization problem by deriving the new revised Newton direction for the sequence of hyperbolic tangent function,then the low sidelobe imaging of sparse array MIMO radar can be obtained.The diagonal loading method and singular value decomposition method are used to improve robustness of the imaging algorithm at the same time.