Joint estimation for DOA and polarization parameters of orthogonal dipole array based on compressive sensing
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摘要:
目的 针对传统的极化敏感阵列的波达方向（DOA）估计算法运算复杂度高、实时性差的问题，提出基于压缩感知的正交偶极子极化敏感阵列结构。 方法 将数据压缩思想应用于阵列结构设计，压缩接收信号矢量维度，减少射频前端链路数量，以控制系统的复杂度，使阵列结构设计具有高度的灵活性。基于结构降维多重信号分类（MUSIC）算法；首先，通过空间谱搜索实现信号的DOA估计；然后，利用拉格朗日乘数法降维；最后，通过解决优化问题获取信号的极化参数信息。 结果 仿真实验表明：采用所提阵列结构及方法在入射信号完全极化且非相干时，可以获得正确的信号DOA和极化参数联合估计；在信噪比（SNR）大于10 dB的环境下，俯仰角均方根误差（RMSE）低于0.05°。 结论 与相同条件下同等通道数的非压缩结构相比，基于压缩感知的正交偶极子阵列参数估计结构的估计精度更高、运算复杂度更低。 Abstract:Objectives As the detection-of-arrival (DOA) estimation algorithm used in traditional polarization sensitive arrays has such problems as high computation complexity and poor real-time performance, this study proposes a data compression-based orthogonal dipole polarization sensitive array structure. Methods By applying compression sensing technology to the system design (i.e., data compression technology), the proposed structure compresses the dimensions of the receiving signal vector, controls the complexity of the system by reducing the number of front-end chains, and brings high flexibility to the array structure design. At the same time, a dimensionality reduction-based multiple signal classification (MUSIC) algorithm is also proposed. First, the DOA estimation of signals is realized through spatial spectrum searching. The Lagrange multiplier method is then used to reduce the searching dimensionality, and the signal polarization parameters are obtained by solving the optimization problem. Results Simulation experiments show that the proposed array structure and MUSIC algorithm can correctly estimate DOA and polarization parameters when the incident signals are completely polarized and incoherent. When the signal-noise ratio (SNR) is greater than 10 dB, the root mean square error (RMSE) of the elevation angle is less than 0.05°. Conclusions Compared with the non-compressed structure with an equal channel number under the same conditions, the proposed structure can provide higher estimation accuracy and lower computational complexity. -
图 3 基于压缩感知的正交偶极子阵列结构示意图
Figure 3. Schematic diagram of data compression based on orthogonal dipole array structure
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