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In this paper, we employ the geometry of uniform circular array to achieve classification and localization of mixed near-field and far-field sources. Considering that the eigendecomposition of the covariance matrix requires high computational cost, we develop the propagator method to obtain the noise subspace and reduce complexity. Firstly, since the direction parameters of far-field sources at centrosymmetry sensors hold a conjugate structure while the covariance matrix of near-field sources holds a Hermitian structure, we exploit the covariance differencing approach to extract the pure near-field sources from mixed sources. Then, we improve the ESPRIT-like method and one-dimensional MUSIC method to determine the 2-D direction-of-arrival (DOA) and range of near-field sources, respectively. Finally, by calculating the noise power of mixed sources, we utilize the oblique projection approach to extract the pure far-field sources and exploit the 2-D MUSIC method to determine the 2-D DOA of far-field sources. Simulations demonstrate that the proposed algorithm can avoid the pseudo-peaks in the 2-D DOA spatial spectrum of far-field sources and provide the satisfactory performance of mixed source localization. [ABSTRACT FROM AUTHOR]

The problem of two dimensional direction of arrival estimation of coherent signals using three parallel uniform linear arrays is investigated. In the proposed method a pre-processing technique, forward and backward spatial smoothing is employed to resolve the coherency. Then the propagator method is exploited for estimating the two dimensional angles with automatic pairing for three parallel uniform linear arrays. Numerical simulations are done to show the efficacy of the proposed method. It validates the estimation accuracy, resolution and robustness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of two-dimensional (2D) direction of arrival (DOA) estimation of multiple signals for generalized coprime planar arrays consisting of two rectangular uniform planar subarrays. We propose a fast-convergence trilinear decomposition approach, which uses propagator method (PM) as the initialization of the angle estimation to speed the convergence of trilinear decomposition. The received signal of each subarray can be fitted into a trilinear model or parallel factor (PARAFAC) model so that the trilinear alternating least square algorithm can be used to estimate the angle information. Meanwhile, the necessary initialization of DOA estimates can be achieved via PM, which endows the proposed approach a fast convergence and subsequently results in a low complexity. Specifically, we eliminate the ambiguous estimates by utilizing the coprime property and the true DOA estimates can be achieved by selecting the nearest ones of all DOA estimates. The proposed approach can obtain the same estimation performance as the conventional PARAFAC algorithm, but with a low computational cost. Numerical simulation results are provided to validate the effectiveness and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of two-dimensional direction of arrival estimation of coherent sources for acoustic vector sensors array. Compared with subspace-based methods, such as root multiple signal classification and estimation of signal parameters via rotational invariance technique, the propagator method (PM) has lower computational complexity. However, only in high-snapshots situation, can the PM algorithm enjoy a better estimation performance. Besides, all of these algorithms mentioned above cannot work for coherent sources. In this paper, we combine PM algorithm with Toeplitz Hermitian matrix reconstruction, and propose an improved algorithm, which works well in the case of coherent signals and a single snapshot. Furthermore, the proposed method can achieve automatically paired two-dimensional angle estimation. Simulation results verify that the proposed method has the better angle performance and less computational complexity than spatial smoothing methods. [ABSTRACT FROM AUTHOR]

This paper considers a new azimuth-elevation DOA estimation algorithm for multiple signals using electromagnetic vector sensor array. We firstly exploit the planar-plus-an-isolated sensor array geometry (Li et al. in IEE Proc Radar Sonar Navig 143(5):295–299, 1996) to define a full rank cross-covariance matrix. Then we develop an efficient ESPRIT-like algorithm using the so-called propagator to estimate the steering vectors of electromagnetic vector sensor, with out performing eigen-decomposition into signal subspaces. Finally, we compute the vector cross product to obtain the closed-form azimuth-elevation angle estimates. The new algorithm does not require 2D iterative searching, and is applicable to coherent (fully correlated) signals and spatially correlated noise. In addition, the proposed algorithm offers enhanced estimation precision by sparse array aperture extension, but suffers no DOA cyclical ambiguity. Monte-Carlo simulations are presented to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

An acoustic vector sensor (AVS) can capture more information than a conventional acoustic pressure sensor (APS). As a result, more output channels are required when multiple AVS are formed into arrays, making processing the data stream computationally intense. This paper proposes a new algorithm based on the propagator method for wideband coherent sources that eliminates eigen-decomposition in order to reduce the computational burden. Data from simulations and lake trials showed that the new algorithm is valid: it resolves coherent sources, breaks left/right ambiguity, and allows inter element spacing to exceed a half-wavelength. [ABSTRACT FROM AUTHOR]