电子学报 ›› 2016, Vol. 44 ›› Issue (6): 1314-1321.DOI: 10.3969/j.issn.0372-2112.2016.06.008

• 学术论文 • 上一篇    下一篇

基于贝叶斯压缩感知的FD-MIMO雷达Off-Grid目标稀疏成像

王天云1,2, 陆新飞2, 丁丽2, 尹治平3, 陈卫东2   

  1. 1. 中国卫星海上测控部, 江苏江阴 214431;
    2. 中国科学技术大学中科院电磁空间信息重点实验室, 安徽合肥 230027;
    3. 合肥工业大学光电技术研究院, 安徽合肥 230009
  • 收稿日期:2014-08-30 修回日期:2015-02-04 出版日期:2016-06-25 发布日期:2016-06-25
  • 通讯作者: 陈卫东
  • 作者简介:王天云 男,1986年出生于河南信阳,中国科学技术大学在读博士研究生,研究方向为分布式雷达稀疏成像、压缩感知技术等.E-mail:wangty@mail.ustc.edu.cn;陆新飞 男,1990年出生于安徽亳州,为中国科学技术大学在读博士研究生,研究方向为高分辨雷达成像、阵列信号处理技术等;丁丽 女,1985年出生于浙江安吉,2014年获中国科学技术大学博士学位,现为上海理工大学讲师,研究方向为MIMO雷达成像、太赫兹成像技术等;尹治平 男,1980年出生于湖北常宁,合肥工业大学副研究员,硕士生导师,研究方向为雷达与微波成像.
  • 基金资助:

    国家自然科学基金(No.61172155,No.61401140,No.61403421);国家863计划项目资助课题(No.2013AA122903)

Bayesian Compressive Sensing-Based Sparse Imaging for Off-Grid Target in Frequency Diverse MIMO Radar

WANG Tian-yun1,2, LU Xin-fei2, DING Li2, YIN Zhi-pin3, CHEN Wei-dong2   

  1. 1. China Satellite Maritime Tracking and Control Department, Jiangyin, Jiangsu 214431, China;
    2. Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China;
    3. Academy of Photoelectric Technology, Hefei University of Technology, Hefei, Anhui 230009, China
  • Received:2014-08-30 Revised:2015-02-04 Online:2016-06-25 Published:2016-06-25

摘要:

传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.

关键词: 贝叶斯压缩感知, FD-MIMO雷达, Off-grid目标, 变分贝叶斯学习, 稀疏自聚焦成像

Abstract:

Conventional compressive sensing (CS) imaging methods rely on the assumption that all scatterers in the imaging scene are located exactly on the pre-defined grids.However, since the scatterers are distributed in a continuous scene, the off-grid problem inevitably exists, which makes basis mismatch between echo measurement and the assumed sensing matrix, and leads to considerable performance degradation by CS-based methods.Therefore, this paper investigates the sparse imaging for off-grid target in frequency diverse multiple-input multiple-output (FD-MIMO) radar.A sparse autofocus imaging method based on Bayesian compressive sensing (SAF-BCS) is proposed.It employs the technique of variational Bayesian inference to achieve the imaging of off-grid scatterres in light of the criterion of maximum a posteriori (MAP).Compared with the conventional sparse recovery algorithms, the proposed method adequately utilizing the prior information of the target, is able to automatically tune parameters, and thus can provide a better capability to correct the off-grid errors, and to estimate the noise power, etc.Simulation results confirm that SAF-BCS is not sensitive to grid discretization, and has a robust imaging performance.

Key words: Bayesian compressive sensing, FD-MIMO radar, off-grid target, variational Bayesian inference, sparse autofocus imaging

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