Research On Linear Array Three-Dimensional Synthetic Aperture Radar Sparse Imaging Technology

As a novel three-dimensional radar imaging technology, linear array three-dimensional synthetic aperture radar (SAR) has great and important value in military and civilian fields, such as high accuracy mapping, earth resources investigation, disasters and environmental monitoring, reconnaissance and surveillance, early warning, etc.. Limited by the traditional Nyquist sampling theorem and the classical signal processing theory, there exist some problems in the application of LASAR3-D imaging currently, including the low resolution, the high sampling ratio, the difficult of system implementation and the large number of echoes storage, transmission and processing, etc. However, as a new signal processing theorem in recent years, compressed sensing breaks the limits of the classical Nyquist sampling theorem. It can recover a sparse signal exactly with the sampled number far lower than that of the Nyquist ratio, and so has great potential in reducing the radar system sampling and improving the quality of radar imaging. Based on the compressed sensing sparse signal processing theorem, this dissertation focuses on the basic imaging principle and method research for the high resolution3-D LASAR sparse imaging, the key problems mainly including LASAR echoes linear representation, sparse reconstruction method, phase errors correction and array antenna distribution optimization, etc. The main works and innovation points are as follow:1. Research on the basic principle of LASAR sparse imaging technology. Exploiting the relationship between the LASAR echoes and the imaging space, the linear measurement models of echo signal in range direction, array plane(azimuth-cross-track plane) and the whole3-D image space are constructed respectively, and then LASAR imaging can be converted into a problem where solving the optimal resolution of the given linear equations. These linear models also provide a new idea for LASAR imaging. Further, combined the space sparsity of scatterers, a novel sparse imaging method based on compressed sensing theorem is proposed for LASAR. In addition, the linear sparse representation of scattering coefficients, the sparse sampling of echoes and the resolution of LASAR sparse imaging are discussed. For the large scale data in LASAR, a separable imaging method on range and array plane is proposed for3-D LASAR sparse imaging. Last, the performance of the classical matched filter method, the least square method and the CS sparse recovery method is analyzed through theoretical deducing.2. Research on sparse reconstruction algorithms for LASAR sparse imaging. First, the classical OMP algorithm is applied to LASAR complex data sparse imaging. For the unknown scatterers sparsity in LASAR imaging, a OMP modified algorithm, named hard threshold OMP (HTOMP) is proposed. By employing the ratio of scattering coefficient change as the iteration stop condition, HTOMP can obtain3-D LASAR image without the scatterer sparsity. Second, the promising BCS algorithm is used for LASAR complex data sparse imaging.in order to reduce the difficult of parameters selection in BCS, base on the exponential distribution of the scattering coefficient, Bayesian theory and maximum likelihood estimation, a sparse Bayesian recovery via iterative minimum (SBRIM) algorithm is proposed for LASAR sparse imaging, wherein, the sparsity estimation method, the adaptive parameter selection method and gradient conjugate method are used to improve the sparse recovery performance. Lastly, combined with the space sparsity of the scatterers in3-D imaging space, a fast sparse recovery method via target location prediction is proposed for LASAR sparse imaging. The effectiveness of LASAR sparse imaging technology and the spare imaging method is verified by some numeral simulation data and the real data obtained ground-based LASAR experimental system.3. Research on LASAR auto focus sparse imaging algorithm. First, the linear measurement models of LASAR echo signal with phase error for different direction are set up, and the phase error estimation in LASAR can be converted into solving solutions of constrain modulus quadratic program. The effect of the different types of phase errors is discussed. Based on the model relaxation and maximum likelihood estimation, the performances of LASAR sparse autofocus imaging with Eigen-value relaxation and semi-definite relaxation are analyzed. For the under-sampled LASAR echo signal, a novel sparse autofocus Bayesian recovery via iterative minimum algorithm is proposed, wherein, the LASAR autofocus sparse imaging with phase errors can be divided into three steps to finding the optimal solution of the linear equations, and the iterative estimation method is used to obtain the optimal scattering coefficients and the phase error estimation. All algorithms are performed by simulated and real experimental data.4. Research on the linear array antenna distribution optimization for LASAR sparse imaging. The relationship between the measurement matrix coherence and the LASAR system ambiguity function is studied through theoretical derivations. The effects of the uniform sparse linear array, non-uniform sparse linear array and random sparse linear array for the LASAR measurement matrix are discussed. Based on the minimum measurement matrix coherence, a distribution optimization method based on the minimum peak and the sidelobe ratio is proposed for non-uniform sparse linear array, and a distribution optimization method based on the minimum variance is proposed for random sparse linear array. Simulation results demonstrate the effectiveness of the both methods.In a word, this dissertation builds the basic principles of LASAR sparse imaging technology, and obtains a series of valuable research results for LASAR sparse imaging algorithm and linear array distribution optimization. The research results provide an important theoretical guidance and technical support for LASAR sparse imaging technology.