一种强干扰环境下的离格稀疏贝叶斯DOA估计方法
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1.南京航空航天大学电子信息工程学院,南京,211106;2.上海交通大学系统控制与信息处理教育部重点实验室,上海,200240

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浙江大学CAD&CG国家重点实验室开放课题(A1906)资助项目 ; 系统控制与信息处理教育部重点实验室基金 Scip201802┫资助项目 浙江大学CAD&CG国家重点实验室开放课题(A1906)资助项目;系统控制与信息处理教育部重点实验室基金(Scip201802)资助项目。


Off-Grid Sparse Bayesian DOA Estimation Method in Strong Interference Environment
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1.College of Electronics and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing,211106,China;2.Key Laboratory of System Control and Information Processing,Ministry of Education,Shanghai Jiao Tong University,Shanghai,200240,China

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    摘要:

    强干扰的环境下,基于传感器阵列的波达方向(Direction of arrival,DOA)估计是阵列信号处理中的重要问题。虽然对于网格点目标现有方法的DOA估计精度较高,但对于离格点目标现有方法的DOA估计性能会严重下降。本文提出一种离格情况下的DOA估计方法,首先设计一种鲁棒的正交零陷矩阵滤波法(Robust orthogonal matrix filter with nulling,ROMFN),它结合了正交零陷滤波法(Orthogonal matrix filter with nulling,OMFN)和最差性能下的鲁棒自适应波束形成,在对离格点目标达到滤波效果的同时只需设计较少的网格点。此外,新的矩阵滤波法保留了高斯白噪声的特性,避免了噪声白化的预处理过程。其次基于离格点稀疏贝叶斯推断(Off-grid sparse Bayesian inference,OGSBI)和ROMFN,形成一种强干扰下DOA估计的新方法。与现有方法相比,仿真结果表明该方法可以在不同的网格间距、不同的信噪比和干噪比下获得更高的估计精度。

    Abstract:

    Direction of arrival (DOA) estimation using sensor arrays with strong interferences is an important problem in array signal processing. Although the DOA estimation accuracy of existing methods for on-grid targets is high, the DOA estimation performance of these methods for off-grid targets degrades seriously. In this paper, a new DOA estimation method is proposed. Firstly, a new matrix filter called robust orthogonal matrix filter with nulling (ROMFN) which combines the orthogonal matrix filter with nulling (OMFN) and robust adaptive beamforming using worst-case is used for filtering of off-grid targets. ROMFN is effective for off-grid targets just with a few grid points designed. Besides, the new matrix filter preserves the white Gaussian noise property, avoiding the pretreatment of noise whitening. Secondly, a new DOA estimation method is used to estimate DOAs of off-grid sources with strong interferences based on off-grid sparse Bayesian inference (OGSBI) and ROMFN. Compared to previous methods, simulation results validate the proposed method can achieve better resolution in different grid intervals, different SNRs and different INRs.

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张赫,陈华伟.一种强干扰环境下的离格稀疏贝叶斯DOA估计方法[J].数据采集与处理,2019,34(6):1019-1029

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  • 收稿日期:2019-05-16
  • 最后修改日期:2019-06-24
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  • 在线发布日期: 2019-12-13