Sparse Bayesian Perspective for Radar Coincidence Imaging with Model Errors

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

doi:10.1155/2020/9202654

Abstract

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.

Highlights

Imaging radar is an outstanding form of remote sensing equipment with many advantages, including its robust performance under all-weather and all-day circumstances, long-distance capabilities, and high probability of target identification [1]
This paper focuses on the sparsity-based radar coincidence imaging (RCI) with model errors
The parametric joint sparse reconstruction model was built to utilize the structured information of model errors; an appropriately structured sparse Bayesian prior was assigned to the sparse coefficients and the sparse Bayesian autofocus (SSBA) algorithm was proposed under the variational Bayesian expectation maximization (VBEM) framework

Summary

Introduction

Imaging radar is an outstanding form of remote sensing equipment with many advantages, including its robust performance under all-weather and all-day circumstances, long-distance capabilities, and high probability of target identification [1]. While RAR can realize staring imaging for targets or regions of interest, the azimuth resolution is limited by the practical antenna aperture; it is difficult to apply RAR to high-resolution imaging applications. The virtual synthetic aperture radar (SAR) [8, 9], which encompasses SAR and inverse SAR (ISAR), exhibits high-resolution ability based on the range-Doppler (RD) principle, where the high resolution generally depends on large signal bandwidth and angle variation. Preliminary theoretical analysis shows that stochastic radiation can lead to superresolution capability, i.e., breaking through the Rayleigh resolution limit of the antenna [12]. RCI can be regarded as a complement of conventional imaging methods such as RAR and SAR/ISAR and can further be employed in some important applications including high-resolution Earth observation, oceanic monitoring, and military reconnaissance

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Conclusion