Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

Xiaoli Zhou; Hongqiang Wang; Yongqiang Cheng; Yuliang Qin

doi:10.1117/1.JEI.25.1.013018

1 February 2016 Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling

Xiaoli Zhou, Hongqiang Wang, Yongqiang Cheng, Yuliang Qin

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Journal of Electronic Imaging, Vol. 25, Issue 1, 013018 (February 2016). https://doi.org/10.1117/1.JEI.25.1.013018

Abstract

Radar coincidence imaging (RCI) is a high-resolution imaging technique without the limitation of relative motion between target and radar. In sparsity-driven RCI, the prior knowledge of imaging model requires to be known accurately. However, the phase error generally exists as a model error, which may cause inaccuracies of the model and defocus the image. The problem is formulated using Bayesian hierarchical prior modeling, and the self-calibration variational message passing (SC-VMP) algorithm is proposed to improve the performance of RCI with phase error. The algorithm determines the phase error as part of the imaging process. The scattering coefficient and phase error are iteratively estimated using VMP and Newton’s method, respectively. Simulation results show that the proposed algorithm can estimate the phase error accurately and improve the imaging quality significantly.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Xiaoli Zhou, Hongqiang Wang, Yongqiang Cheng, and Yuliang Qin "Radar coincidence imaging with phase error using Bayesian hierarchical prior modeling," Journal of Electronic Imaging 25(1), 013018 (1 February 2016). https://doi.org/10.1117/1.JEI.25.1.013018

Published: 1 February 2016

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