Two-Dimensional DOA Estimation for L-shaped Nested Array via Tensor Modeling
Feng Xu, Sergiy A. Vorobyov

TL;DR
This paper introduces an iterative tensor-based method for 2-D DOA estimation using L-shaped nested arrays, effectively handling correlated signals and surpassing traditional methods in resolution and accuracy.
Contribution
It develops a novel tensor modeling approach with an efficient decomposition and iterative refinement to improve 2-D DOA estimation for L-shaped nested arrays.
Findings
Achieves higher resolution than conventional methods.
Can resolve more sources than the number of array elements.
Demonstrates improved accuracy through simulations.
Abstract
The problem of two-dimensional (2-D) direction-of-arrival (DOA) estimation for the L-shaped nested array is considered. Typically, the multi-dimensional structure of the received signal in co-array domain is ignored in the problem considered. Moreover, the cross term generated by the correlated signal and noise components degrades the 2-D DOA estimation performance seriously. To tackle these issues, an iterative 2-D DOA estimation approach based on tensor modeling is proposed. To develop such approach, a higher-order tensor is constructed, whose factor matrices contain the sources azimuth and elevation information. By exploiting the Vandermonde structure of the factor matrix, a computationally efficient tensor decomposition method is then developed to estimate the sources DOA information in each dimension independently. The pair-matching of the azimuth and elevation angles is conducted…
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Taxonomy
TopicsDirection-of-Arrival Estimation Techniques · Advanced Adaptive Filtering Techniques · Speech and Audio Processing
