A Maximum Likelihood Method for Joint DOA and Polarization Estimation Based on Manifold Separation

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 paper, the manifold separation steering vector modeling technique is used to develop a maximum likelihood (ML) 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 Multiple Signal Classification (PES-MUSIC) method and the polarimetric Capon (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 propsoed method more suitable for non-stationary target polarization. The performance of the proposed method is demonstrated through simulations. The Cramer-Rao Lower Bound (CRLB) for joint DOA and polarization is also used for comparison with empirical errors.