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Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference.

A new interesting article has been published in Sensors (Basel). 2018 Oct 20;18(10). pii: E3563. doi: 10.3390/s18103563. and titled:

Three-Dimensional Imaging Method for Array ISAR Based on Sparse Bayesian Inference.

Authors of this article are:

Jiao Z,, Ding C,, Chen L,, Zhang F0.

A summary of the article is shown below:

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.

Check out the article’s website on Pubmed for more information:



This article is a good source of information and a good way to become familiar with topics such as:

array ISAR;elastic net regression;sparse Bayesian inference;synthesis scatterers;three-dimensional imaging

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