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Using the dual-polarized array for underdetermined estimation of partially polarized (PP) signal parameters can lead to limited signal-to-ratio (SNR) and biased reconstruction of the coherency matrix. In this paper, a new non-iterative method is proposed with the tri-polarized nested array. With the sub-covariance addition, the power of different polarized components of a signal can be completely accumulated, which improves the SNR. Besides, it is proven that with the optimized tri-polarized nested array, noise variance estimation without iterations becomes possible in the underdetermined case, which is critical for unbiased coherency matrix reconstruction. The subspace-based method is adopted to estimate the direction-of-arrival (DOA), and the polarization parameters can be obtained based on the reconstructed coherency matrices. The proposed method is validated by numerical experiments and compared with other representative methods. It has relatively high accuracy and is about one order of magnitude faster than its competitor.

Using the dual-polarized array for underdeter-mined estimation of partially polarized (PP) signal parameters can lead to limited signal-to-ratio (SNR) and biased reconstruction of the coherency matrix. In this paper, a new non-iterative method is proposed with the tri-polarized nested array. With the sub-covariance addition, the power of different polarized components of a signal can be completely accumulated, which improves the SNR. Besides, it is proven that with the optimized tri-polarized nested array, noise variance estimation without iterations becomes possible in the underdetermined case, which is critical for unbiased coherency matrix reconstruction. The suhspace-hased method is adopted to estimate the direction-of-arrival (DOA), and the polarization parameters can he obtained based on the reconstructed coherency matrices. The proposed method is validated by numerical experiments and compared with other representative methods. It has relatively high accuracy and is about one order of magnitude faster than its competitor. [ABSTRACT FROM AUTHOR]

This letter considers the problem of underdetermined estimation of direction-of-arrival (DOA) and polarization parameters, including the degree of polarization (DOP), polarization orientation angle (POA), and polarization ellipticity angle (PEA), for partially polarized (PP) signals with a dual-polarized coprime array. Firstly, the increased dimension due to dual-polarized sensors is reduced by adding two covariance matrices, which facilitates the coarray construction. Then, the holes in the coarray of coprime array are interpolated via utilizing the annihilating relation. At last, the DOAs and array noise variance are obtained by solving an annihilating relation based optimization problem and then used to reconstruct the coherency matrices via the weight least squares (WLS). The polarization parameters can be estimated by performing eigendecomposition on the reconstructed coherency matrices. We show the underdetermined estimation of array noise variance which is difficult for conventional methods is vital for accurate underdetermined estimation of the polarization parameters. The performances of the proposed method are verified by numerical simulations and compared with other representative methods including the Cramer-Rao bound (CRB). [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of source localization and classification under the coexistence of both completely polarized (CP) and partially polarized (PP) electromagnetic (EM) signals, using a crossed-dipole sensor array. We propose a MUltiple SIgnal Classification (MUSIC)-based solution, which does not require multidimensional searches. Moreover, the proposed method need no estimation of the degree of polarization of signals. The efficacy of the proposed method is examined by comparing with existing methods.

This paper illustrates the full use of an orthogonal decoupling signal model to describe the 3-D construction of polarized electromagnetic waves. The maximum likelihood method and MUSIC algorithm were introduced into the parameter estimation of partially polarized waves. Attitude reference information for an aircraft that was calibrated by an information base station was delivered by polarized electromagnetic waves. Through receiving and processing the information of the aircraft, a single electromagnetic vector sensor can acquire the aircraft attitude in a geographic coordinate system. Differing from the triangle calculation method which relies on a multi-point measurement, the proposed method only needs a base station signal and a single receiver on the motion platform to realize the posture perception of aircraft. It can therefore serve as a substitute for aircraft attitude navigation and aircraft heading navigation. The introduction of the motion platform attitude estimation algorithm provides a technology support for engineering applications.

In this paper, we investigate the problem of source localization and classification under the coexistence of both completely polarized (CP) and partially polarized (PP) electromagnetic (EM) signals, using a crossed-dipole sensor array. We propose a MUltiple SIgnal Classification (MUSIC)-based solution, which does not require multidimensional searches. Moreover, the proposed method need no estimation of the degree of polarization of signals. The efficacy of the proposed method is examined by comparing with existing methods.