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首页> 外文期刊>IEEE Transactions on Aerospace and Electronic Systems >A Maximum Likelihood Method for Joint DOA and Polarization Estimation Based on Manifold Separation
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A Maximum Likelihood Method for Joint DOA and Polarization Estimation Based on Manifold Separation

机译：基于歧管分离的关节DOA和极化估计的最大似然方法

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摘要

The use of the polarization diversity of a target signal at a polarization-sensitive antenna array can enhance the target detection and tracking capabilities of a radar. In this article, the manifold separation steering vector modeling technique is used to develop a maximum likelihood method for joint direction of arrival (DOA) and polarization estimation. Manifold separation can incorporate antenna array nonideal characteristics (e.g., cross polarization, mutual coupling) into the estimation algorithm using array calibration measurements. In the proposed technique, the estimation problem is formulated as a generalized Rayleigh quotient minimization problem that is transformed into a determinant minimization problem. Both the azimuth and elevation angles are estimated using the fast Fourier transform. Unlike the existing manifold separation based polarimetric element space (PES) multiple signal classification method and the PES Capon method, the proposed method can obtain DOA and polarization estimates based on very small-size primary data samples, even with a single sample, which makes the proposed method more suitable for nonstationary target polarization. The performance of the proposed method is demonstrated through simulations. The Cramer-Rao lower bound for joint DOA and polarization is also used for comparison with empirical errors.
机译：在极化敏感天线阵列处使用目标信号的偏振分集可以增强雷达的目标检测和跟踪能力。在本文中，歧管分离转向矢量建模技术用于开发用于联合到达的最大似然方法（DOA）和极化估计。歧管分离可以使用阵列校准测量将天线阵列非膜特性（例如，交叉极化，相互耦合）合并到估计算法中。在所提出的技术中，将估计问题制定为广义瑞利商的最小化问题，该问题被转换为决定性最小化问题。方位角和高度角度都是使用快速傅里叶变换估计的。与基于现有的歧管分离的偏振元件空间（PES）多信号分类方法和PES Capon方法不同，所提出的方法可以基于非常小的主要数据样本获得DOA和偏振估计，即使是单个样本，也可以使其成为提出的方法更适合于非营养的目标极化。通过模拟证明了所提出的方法的性能。接头DOA和极化的克拉默 - RAO下限也用于与经验误差进行比较。

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