ISAR Motion Compensation And Imaging Technologies Based On Sparse Signal Processing

Inverse Synthetic Aperture Radar(ISAR)imaging technology can obtain high-resolution images of non-cooperative moving targets with a stationary radar system.Because of its all-day,all-weather and long-distance advantages,ISAR plays an important role in both civil and military fields.The research about ISAR mainly focuses on two aspects.The first one is motion compensation method,which is the precondition for high quality ISAR imaging.Robust and efficient motion compensation method can extend the application scenario of ISAR imaging technology and improve its practicability.The second one is the imaging algorithm.Accurate and high-resolution imaging algorithm can reduce the requirement of hardware systems.Based on the natural sparsity of ISAR imaging scene,this dissertation studies the motion compensation and imaging technology of ISAR with the sparse signal processing method.The specific research works are organized as follows:Firstly,the translational compensation for ISAR imaging based on sparse signal processing is studied.We establish the corresponding signal model and analyze the translational effects on the ISAR echo.By fitting the translational trajectory into the form of a polynomial,the translational compensation is transformed into an optimization problem for both the sparse scene and the polynomial coefficients.We achieve the translational compensation by solving the optimization problem.In addition,the existing ISAR autofocus algorithms based on sparse signal processing have the defects of large computational complexity and poor performance under high under-sampling rate.To tackle this problem,we propose an efficient autofocus method based on modified sparse Bayesian learning algorithm.By exploiting the block-sparse structure of the targets,the algorithm achieves better performance comparing with existing methods.Experimental results have demonstrated the effectiveness of our proposed algorithm.Secondly,we study the ISAR two-dimensional rotating target imaging algorithm based on sparse signal processing.For ISAR targets with non-uniform rotation,their Doppler frequencies will change with time,which result in the azimuth-dimensional blurring of the imaging results.This dissertation studies the imaging problem of non-uniform rotating targets under sparse sampling conditions.We consider the influence of the target rotational acceleration and establish the corresponding signal model.An algorithm is proposed to achieve the imaging of non-uniform rotating targets and the estimation of motion parameters.In addition,with the increase of rotation angle,the high-order phase and migration through range cells(MTRC)will also seriously affect the imaging quality.By analyzing the echo signals and establishing corresponding models,we propose a new algorithm to estimate the rotational motion parameters and recover the target's^image iteratively.Simulation experiments show that the proposed algorithms can obtain high quality images of non-uniform rotating targets.Thirdly,we study the ISAR imaging of targets in maneuvering three-dimensional motions.The rotation of the maneuvering targets is not limited on a two-dimensional plane,so the the traditional rotation compensation method is no longer applicable.By analyzing the echo signals of this kind of targets,we find that their effective rotation axes and imaging plane change with time.To tackle this problem,this dissertation proposes two imaging methods.The first method is to select the appropriate data frame based on the Doppler frequencies of the prominent scatterers in the echo for imaging.In the selected data frame,the Doppler frequencies of the scattering points are approximately constant,the target can be approximated to uniformly rotate around a fixed axis.The second method is to use the Range Instantaneous Doppler(RID)algorithm to obtain the instantaneous image of the target.We take advantage of the sparsity of the spectrogram and the similarity of the Doppler frequencies between adjacent range bins,and convert the time-frequency analysis in RID into a sparse signal recovery problem.The simulations show the effectiveness of the proposed algorithm.