DOA Estimation Technology Based on Khatri-Rao Product for 3D Forward-looking Sonar
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摘要: 为了提高3维前视声呐的方位分辨能力，同时避免2维(2D)方位估计(DOA)方法失效，该文提出1维(1D)空间角估计方法、基于Vernier法的垂直角估计方法和基于最小角定理的水平角方位估计方法。首先基于不同子阵构造互协方差矩阵避免2维方位估计模型失效，再利用Khatri-Rao积进行虚拟孔径扩展；将扩展后的阵列导向矢量和观测向量模型用于2维方位估计。与原阵列的导向矢量相比，虚拟阵元数量约增加1倍，阵列的孔径得到有效扩展。仿真实验表明，与单观测向量波束形成2维方位估计方法相比，所提方法在2维方位估计问题中具有更高的分辨能力，均方根误差更低；水池实验进一步验证了该文所提方法的工程实用性。
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关键词:
- 3维前视声呐 /
- 2维方位估计 /
- 虚拟孔径扩展 /
- Khatri-Rao积 /
- Vernier法
Abstract: In order to obtain higher resolution and avoid the failure of Two-Dimensional (2D) of Direction-Of-Arrival (DOA) estimation, One-Dimensional (1D) spatial DOA estimation method, vertical DOA estimation via Vernier method and horizontal DOA estimation method via minimum angle theorem are proposed. First, covariance matrices are constructed based on various subarrays to alleviate the failure of 2D model, and the Khatri-Rao product is adopted to extend the virtual array aperture. Second, the extended observation models and corresponding array steer vector are exploited for 2D DOA estimation. Compared with the steer vector of the original array, the number of virtual array elements is doubled, and thus the array aperture is extended. Simulation results show that the proposed method has better resolution and lower RMSE performance in 2D DOA estimation problem compared with the Single Measured Vector Beamforming method. The tank experiment further verifies the engineering practicability of the proposed method. -
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