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DOA estimation exploiting a uniform linear array with multiple co-prime frequencies

机译：利用具有多个互质数频率的均匀线性阵列进行DOA估计

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摘要

The co-prime array, which utilizes a co-prime pair of uniform linear sub-arrays, provides a systematical means for sparse array construction. By choosing two co-prime integers M and N, O(MN) co-array elements can be formed from only O(M + N) physical sensors. As such, a higher number of degrees-of-freedom (DOFs) is achieved, enabling direction-of-arrival (DOA) estimation of more targets than the number of physical sensors. In this paper, we propose an alternative structure to implement co-prime arrays. A single sparse uniform linear array is used to exploit two or more continuous-wave signals whose frequencies satisfy a co-prime relationship. This extends the co-prime array and filtering to a joint spatio-spectral domain, thereby achieving high flexibility in array structure design to meet system complexity constraints. The DOA estimation is obtained using group sparsity-based compressive sensing techniques. In particular, we use the recently developed complex multitask Bayesian compressive sensing for group sparse signal reconstruction. The achievable number of DOFs is derived for the two-frequency case, and an upper bound of the available DOFs is provided for multi-frequency scenarios. Simulation results demonstrate the effectiveness of the proposed technique and verify the analysis results.
机译：利用均匀线性子阵列的互质对的互质阵列为稀疏阵列的构建提供了系统的手段。通过选择两个互质整数M和N，可以仅由O（M + N）个物理传感器形成O（MN）个共阵列元素。这样，可以实现更多的自由度（DOF），从而可以实现比物理传感器数量更多的目标的到达方向（DOA）估计。在本文中，我们提出了另一种结构来实现互素数组。单个稀疏均匀线性阵列用于开发两个或多个连续波信号，其频率满足互素关系。这样可以将互质数阵列和滤波扩展到联合的空间光谱域，从而在阵列结构设计中实现高度灵活性，以满足系统复杂性的约束。使用基于组稀疏性的压缩感测技术获得DOA估计。特别是，我们将最近开发的复杂多任务贝叶斯压缩感测用于组稀疏信号重建。针对两频情况导出了可达到的DOF数量，并为多频方案提供了可用DOF的上限。仿真结果证明了该技术的有效性，并验证了分析结果。

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  • 来源
    《Signal processing》 |2017年第1期|37-46|共10页
  • 作者

    Si Qin; Yimin D. Zhang; Moeness G. Amin; Braham Himed;

  • 作者单位

    Wireless Communications and Positioning Laboratory, The Center for Advanced Communications, Villanova University, Villanova, PA 19085, USA;

    Department of Electrical and Computer Engineering, College of Engineering, Temple University, Philadelphia, PA 19122, USA;

    Wireless Communications and Positioning Laboratory, The Center for Advanced Communications, Villanova University, Villanova, PA 19085, USA;

    RF Technology Branch, Air Force Research Lab (AFRL/RYMD), WPAFB, OH 45433, USA;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    DOA estimation; Co-prime array; Sparse sampling; Group sparsity; Sparse Bayesian learning;

    机译：DOA估算;互素数组;稀疏采样;小组稀疏;稀疏贝叶斯学习;

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