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.

中文翻译：

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一种基于流形分离的联合DOA和极化估计的最大似然法

在极化敏感天线阵列上使用目标信号的极化分集可以增强雷达的目标检测和跟踪能力。在本文中，流形分离导向矢量建模技术用于开发联合到达方向 (DOA) 和极化估计的最大似然方法。流形分离可以使用阵列校准测量将天线阵列的非理想特性（例如，交叉极化、互耦合）合并到估计算法中。在所提出的技术中，估计问题被表述为广义瑞利商最小化问题，该问题转化为行列式最小化问题。方位角和仰角都是使用快速傅立叶变换来估计的。与现有的基于流形分离的极化元空间（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原始数据样本获得DOA和极化估计，即使是单个样本，这使得所提出的方法更适合于非平稳目标极化。通过仿真证明了所提出方法的性能。联合 DOA 和极化的 Cramer-Rao 下限也用于与经验误差进行比较。这使得所提出的方法更适用于非平稳目标极化。通过仿真证明了所提出方法的性能。联合 DOA 和极化的 Cramer-Rao 下限也用于与经验误差进行比较。这使得所提出的方法更适用于非平稳目标极化。通过仿真证明了所提出方法的性能。联合 DOA 和极化的 Cramer-Rao 下限也用于与经验误差进行比较。