Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

Joint Authors

Zhou, Xiaoli
Cheng, Yongqiang
Fan, Bo
Chen, Shuo
Jiang, Zhijie

Source

Mathematical Problems in Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-04-21

Country of Publication

Egypt

No. of Pages

12

Main Subjects

Civil Engineering

Abstract EN

Sparsity-driven methods are commonly applied to reconstruct targets in radar coincidence imaging (RCI), where the reference matrix needs to be computed precisely and the prior knowledge of the accurate imaging model is essential.

Unfortunately, the existence of model errors in practical RCI applications is common, which defocuses the reconstructed image considerably.

Accordingly, this paper aims to formulate a unified framework for sparsity-driven RCI with model errors based on the sparse Bayesian approach.

Firstly, a parametric joint sparse reconstruction model is built to describe the RCI when perturbed by model errors.

The structured sparse Bayesian prior is then assigned to this model, after which the structured sparse Bayesian autofocus (SSBA) algorithm is proposed in the variational Bayesian expectation maximization (VBEM) framework; this solution jointly realizes sparse imaging and model error calibration.

Simulation results demonstrate that the proposed algorithm can both calibrate the model errors and obtain a well-focused target image with high reconstruction accuracy.

American Psychological Association (APA)

Fan, Bo& Zhou, Xiaoli& Chen, Shuo& Jiang, Zhijie& Cheng, Yongqiang. 2020. Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1202059

Modern Language Association (MLA)

Fan, Bo…[et al.]. Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1202059

American Medical Association (AMA)

Fan, Bo& Zhou, Xiaoli& Chen, Shuo& Jiang, Zhijie& Cheng, Yongqiang. Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1202059

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1202059