Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

Xingyu He; Ningning Tong; Xiaowei Hu

doi:10.1117/1.JRS.12.015013

27 February 2018 Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

Xingyu He, Ningning Tong, Xiaowei Hu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 1, 015013 (February 2018). https://doi.org/10.1117/1.JRS.12.015013

Abstract

Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation–maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.

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Xingyu He, Ningning Tong, and Xiaowei Hu "Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors," Journal of Applied Remote Sensing 12(1), 015013 (27 February 2018). https://doi.org/10.1117/1.JRS.12.015013

Received: 13 September 2017; Accepted: 30 January 2018; Published: 27 February 2018

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