Abstract
The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.
Highlights
With further investigation of space resources in recent years, the surveillance of space target has gained much more attention among institutes around the world
The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method
In [7], 3-D inverse synthetic aperture radar (ISAR) imaging method with a sparse array is proposed and the CLEAN technique is adopted to extract strong artificial scatterers caused by high sidelobes
Summary
With further investigation of space resources in recent years, the surveillance of space target has gained much more attention among institutes around the world. The sparse assumption here is based on the results in [11,26] and the 3D reconstruction result with measured radar data prove its validity Under this framework, multi-channel ISAR images are firstly obtained by using a sparse linear array, which will reduce the imaging duration compared with the sequence ISAR technique. After image registration and phase compensation, the elastic net estimation [28] along with Bayesian information criterion (BIC) [29] is applied to estimate the number of dominant scatterers without any prior information With this estimation as a prior, the sparse Bayesian inference method [30] is used to obtain an accurate estimation of the reflectivity function along the elevation direction. Equation (3), the dominant scatterers in the same range-Doppler unit can be successfully distinguished and the 3D model of targets can be obtained
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
