Yielding better estimation performance for two-dimensional (2-D) direction-of-arrival (DOA) estimation at limited snapshots and low signal-to-noise ratios (SNRs) has recently attracted increasing attention. Aiming at ...
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Yielding better estimation performance for two-dimensional (2-D) direction-of-arrival (DOA) estimation at limited snapshots and low signal-to-noise ratios (SNRs) has recently attracted increasing attention. Aiming at this, a 2-D DOA estimation algorithm based on discrete fractional Fourier transform and Taylor expansion approximation (DFRFT-TEA) for the l-shaped nested array (lsNA) is proposed. Specifically, the discrete fractional Fourier transform (DFRFT) method is first introduced to generate the initial estimated angles, then the Taylor expansion approximation (TEA) method is utilized to compensate the angular offsets to obtain the fine estimated angles, and finally the pair-matching of the fine estimated angles is achieved by the permutation matrix. The findings of the numerical simulation show that the DFRFT-TEA algorithm not only has favorable DOA estimation performance under finite snapshots and low SNRs but also can be adapted to DOA estimation for the underdetermined scenario. Furthermore, the proposed DFRFT-TEA algorithm provides superior estimation performance in comparison to the existing algorithms.
Among various sensor array configurations, the l-shaped nested array offers improved performance for 2-D direction-of-arrival (DOA) estimation through co-array processing. However, conventional methods overlook the mu...
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Among various sensor array configurations, the l-shaped nested array offers improved performance for 2-D direction-of-arrival (DOA) estimation through co-array processing. However, conventional methods overlook the multidimensional signal structure and fail to eliminate the cross term generated from the correlated co-array signal and noise components. It leads to a significant degradation in DOA estimation performance. To deal with this problem, an iterative 2-D DOA estimation algorithm based on tensor modeling is proposed. It is capable of eliminating the cross term. Specifically, the co-array signals of virtual subarrays in orthogonal directions are derived and concatenated to construct a higher order tensor, whose factor matrices have the Vandermonde structure and preserve the interconnected azimuth and elevation information. A computationally efficient tensor decomposition method is then developed to independently estimate the azimuth and elevation angles, which are effectively paired using the spatial cross-correlation matrix. Furthermore, after investigating the cross term effect, a two-step iterative algorithm is proposed to sequentially estimate and remove the cross term based on the initial estimates obtained from the high-order tensor decomposition. Consequently, the 2-D DOA estimation with enhanced estimation accuracy, resolution, and moderate computational complexity is achieved for the l-shaped nested array. Simulation results demonstrate the superiority of the proposed algorithm over competing methods.
In this letter, we devise a new approach for two-dimensional (2-D) direction finding using an l-shapedarray structured by two nestedarrays. By vectorizing the covariance matrices of the nestedarray outputs, the ele...
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In this letter, we devise a new approach for two-dimensional (2-D) direction finding using an l-shapedarray structured by two nestedarrays. By vectorizing the covariance matrices of the nestedarray outputs, the elevation and azimuth angles of the sources are firstly estimated separately. The pair-matching of the elevation and azimuth angle estimates is then achieved employing a permutation matrix which is obtained by solving an optimization problem with the array manifold matrices, cross-covariance matrix, and the source covariance matrix. Unlike the existing schemes, the proposed approach reconstructs the source covariance matrix in the coarray domain. Therefore, it can deal with more sources than the number of sensors in each nestedarray. Numerical results demonstrate the advantage of the proposed approach over the existing algorithms.
The problem of two-dimensional (2-D) direction of arrival (DOA) estimation for l-shaped nested array is considered, and an iterative approach based on tensor modeling is proposed. To develop such an approach, a high-o...
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ISBN:
(纸本)9798350325744
The problem of two-dimensional (2-D) direction of arrival (DOA) estimation for l-shaped nested array is considered, and an iterative approach based on tensor modeling is proposed. To develop such an approach, a high-order tensor is designed to collect the received signals of the subarrays in difference co-array domain. By exploiting the Vandermonde structure of the factor matrices, the sources azimuth and elevation angles can be estimated via tensor decomposition. The cross term of the received signal, which seriously degrades the estimation performance, can be estimated and removed by iterative procedure using also the DOA estimates obtained by tensor decomposition. Consequently, the 2-D DOA estimation performance can be improved gradually after sufficiently many iterations. Simulation results validate the proposed 2-D DOA estimation approach and demonstrate its effectiveness.
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