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In this paper, a sparse reconstruction based approach to estimate Stokes parameters and 2-D DOAs of polarized sources is proposed in case of unknown number of sources. An L-shaped coprime array composed of dipole-dipole pairs, where a dipole in the dipole-dipole pair is placed on x-axis and the other is placed on z-axis, is used to reduce the mutual coupling across two dipoles in the dipole-dipole pair. In order to estimate the elevation of polarized sources, an orthogonal matching pursuit algorithm with the identification of the sources' number (OMP-isn) is proposed. In our proposed approach, the quasi deterministic maximum likelihood (qDML) test step is added in each iteration of algorithm. In addition, a refining grid step is also added in each iteration to reduce the estimation error incurred by the grid mismatch. The OMP algorithm and sparse iterative covariance-based estimation (SPICE) method are employed to estimate the azimuth of polarized sources. In order to pari the azimuth with the elevation, a pair-matching method is proposed based on the l 2 -norm and l 1 -norm ( l 2 / l 1 ) minimization. Based on the pairing azimuth and elevation, Stokes parameters of polarized sources are estimated by using Least Squares (LS) or Total Least Squares (TLS). The stochastic CRB is derived for the 2-D DOAs and Stokes parameters of polarized sources. Simulations are used to verify the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

For wideband array signals, existing self-calibration methods use a first order approximation to simplify the estimation procedure under the assumption of small sensor position errors. However, the performance will degrade severely as the position errors increase. In this paper, a novel self-calibration method based on the time-frequency distributions (TFDs) is proposed to relieve the small errors constraint. We firstly develop an approach for wideband signals to select the TFDs of each source. Then the TFDs are utilized to obtain the propagation delays, which have a simple linear relationship with the sensor positions. The direction of arrival (DOA) and positions are finally estimated by a two-step iteration without any approximation. Simulation results demonstrate that the proposed method can achieve accurate DOA and position estimation over a broad range of position errors with lower computational complexity, and outperforms existing self-calibration algorithms. [ABSTRACT FROM AUTHOR]

For antenna array with large number of elements, the multistage Wiener filter (MWF) is an efficient method to perform reduced-rank beamforming. Similar to the generalized sidelobe canceller (GSC), MWF is also sensitive to direction of arrival (DOA) mismatch of the signal of interest (SOI). This condition limits the application of MWF to monopulse beamforming, in which the actual DOA of SOI may mismatch the assumed direction (i.e. the pattern look direction) due to target movement and platform disturbance. In this paper, we develop a variable-loaded multistage Wiener filter with spatial blocking broadening and automatic rank selection (VLSRS-MWF) to perform robust reduced-rank monopulse beamforming. Firstly, a null-broadened spatial blocking matrix which can be used for both the sum and difference beams is developed instead of the derivative constraints and the eigenvector constraints based blocking matrices. Secondly, a l 2 -norm regularization method is incorporated with MWF to further improve the output signal to noise ratio and to restrict the pattern sidelobe level. Then a Ritz value criterion with deterministic threshold for selecting the rank of the reduced-rank subspace is developed utilizing the eigenvalue distribution of sample covariance matrix and the Ritz value criterion. Simulation results show that the proposed VLSRS-MWF based monopulse algorithm can maintain good robustness and high angle estimation accuracy in presence of DOA mismatch. [ABSTRACT FROM AUTHOR]

In this paper, a novel localization algorithm for mixed far-field and near-field sources is proposed in the presence of unknown mutual coupling. Based on the principle of rank reduction, direction-of-arrival (DOA) estimates of far-field sources are firstly decoupled under unknown mutual coupling. Then these estimates are employed to generate the mutual coupling coefficients. Finally, by the mutual coupling compensation and the far-field components elimination, near-field sources parameters (DOA and range) are obtained. The proposed algorithm is efficient in that it only requires second order statistics and one dimensional spectral search. Simulation results demonstrate that our algorithm is effective for the classification and localization of mixed sources under unknown mutual coupling. [ABSTRACT FROM AUTHOR]