| Inverse Synthetic Aperture Radar(ISAR)radar imaging has the unique advantages of being less affected by environment,all-day/all-weather,and high resolution.It has been playing an important role in civil and military fields in recent years.However,for the sparse aperture signal with missing echo,the coherence between pulses is destroyed,which poses a new challenge to the traditional motion compensation and imaging algorithms.Since ISAR imaging scenes usually meet the conditions of sparsity,compressed sensing(CS)can be applied to recover ISAR sparse data.At present,most of the existing reconstruction algorithms meet the requirements of reconstruction accuracy,but the calculation time needs to be improved.Based on this background,this paper uses sparse signal processing technology to study the phase correction technology and imaging technology in ISAR motion compensation,so as to improve the practical ability of ISAR under the condition of sparse data.The research contents of this paper mainly include:First,the ISAR motion compensation algorithm under sparse signals is studied.In this paper,the principle of two-dimensional high-resolution imaging of ISAR is firstly analyzed,and then the corresponding sparse signal model is established based on it.The problems of translational compensation and phase correction are analyzed,and the phase correction problem is transformed into an optimization problem of estimating phase error.Aiming at the problem of the estimation accuracy of ISAR self-focusing algorithm under the existing sparse signals,this paper added the modified Newton method on the basis of the minimum entropy self-focusing,ensured the convergence along the correct direction through the modified Hessen matrix.On this basis,a corresponding simulation experiment is carried out to verify the effectiveness of the proposed algorithm.Second,the sparse signal reconstruction algorithm in radar imaging is studied.Orthogonal Matching Pursuit(OMP)algorithm,as a classical Matching Pursuit algorithm,has the characteristics of good reconstruction effect and fast computation speed,but its computational efficiency is still insufficient in the practical application of sparse ISAR.The reconstruction process of OMP depends on signal sparsity,and only one value is selected for each inner product calculation,resulting in a lot of computational redundancy.In this paper,the selection mechanism of OMP is studied,and an improved OMP algorithm based on Constant False Alarm Rate(CFAR)threshold is proposed based on the threshold thought of Stagewise Weak Orthogonal Matching Pursuit(SWOMP)algorithm.In the single cycle structure,the threshold is set by the CFAR to complete the selection of atoms.Simulation experiments are provided to verify the performance of the proposed algorithm,which can effectively reduce the computation time on the premise of guaranteeing the reconstruction accuracy.Finally,the sparse ISAR imaging algorithm for stationary aircraft targets is studied,and the sparse ISAR imaging process based on improved OMP algorithm is proposed.Through simulation and measured data,the reconstruction effect and calculation time of improved OMP and other algorithms under different data volumes are compared,the reconstruction effect and calculation time under different signal-to-noise ratios are also compared.The results show that the algorithm proposed in this paper is effective and has certain practical value. |
