Direction of Arrival Estimation using EM-ESPRIT with Nonuniform Arrays

Kassis, Carine El; Picheral, José; Fleury, Gilles; Mokbel, Chafic

doi:10.1007/s00034-012-9397-y

Cited by 8 publications

(2 citation statements)

References 12 publications

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“…, 1 ) are corresponding eigenvectors. According to [31], the estimate of noise variance 2 can be obtained by averaging the 1 − smallest eigenvalues. Similarly, the estimation of the average power of sources 2 can also be obtained by averaging the largest eigenvalues.…”

Section: Proposed Array Interpolation Based On Compressed Sensing Formentioning

confidence: 99%

Direction-of-Arrival Estimation for Coprime Array Using Compressive Sensing Based Array Interpolation

Liu

Yang

Xin

et al. 2017

International Journal of Antennas and Propagation

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A method of direction-of-arrival (DOA) estimation using array interpolation is proposed in this paper to increase the number of resolvable sources and improve the DOA estimation performance for coprime array configuration with holes in its virtual array. The virtual symmetric nonuniform linear array (VSNLA) of coprime array signal model is introduced, with the conventional MUSIC with spatial smoothing algorithm (SS-MUSIC) applied on the continuous lags in the VSNLA; the degrees of freedom (DoFs) for DOA estimation are obviously not fully exploited. To effectively utilize the extent of DoFs offered by the coarray configuration, a compressing sensing based array interpolation algorithm is proposed. The compressing sensing technique is used to obtain the coarse initial DOA estimation, and a modified iterative initial DOA estimation based interpolation algorithm (IMCA-AI) is then utilized to obtain the final DOA estimation, which maps the sample covariance matrix of the VSNLA to the covariance matrix of a filled virtual symmetric uniform linear array (VSULA) with the same aperture size. The proposed DOA estimation method can efficiently improve the DOA estimation performance. The numerical simulations are provided to demonstrate the effectiveness of the proposed method.

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Section: Proposed Array Interpolation Based On Compressed Sensing Formentioning

confidence: 99%

Direction-of-Arrival Estimation for Coprime Array Using Compressive Sensing Based Array Interpolation

Liu

Yang

Xin

et al. 2017

International Journal of Antennas and Propagation

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“…During the last several decades, various subspace-based schemes have been proposed for DoA estimation such as multiple signal classification algorithms [1], estimation of signal parameter via rotational invariance technique (ESPRIT) [2], and root-MUSIC method [3]. Recently, because of their high resolution, these algorithms have been studied in various applications such as nonlinear arrays [4][5][6], 2-dimensional arrays [7,8], and MIMO radar [9]. However, early subspacebased algorithms generally suffer from the issue of large computational complexity, typically due to the need for eigenvalue decomposition (ED) or singular-value decomposition (SVD).…”

Section: Introductionmentioning

confidence: 99%

Direct Signal Space Construction Luenberger Tracker with Quadratic Least Square Regression for Computationally Efficient DoA Tracking

Byeon

Lee

Yoo

2020

Mathematical Problems in Engineering

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In this paper, we propose a computationally efficient direction-of-arrival (DoA) tracking scheme called the direct signal space construction Luenberger tracker (DSPCLT) with quadratic least square (QLS) regression. Also, we study analytically how the choice of observer gain affects the algorithm performance and illustrate how we can use the theoretical results in determining optimal observer gain value. The proposed scheme (DSPCLT) has several distinct features compared with existing algorithms. First, it requires only a fraction of computational complexity compared with other schemes. Secondly, it maintains robustness by treating separately the special case of object overlap in which subspace-based algorithms often suffer from lack of resolvability. Thirdly, the proposed scheme achieves enhanced performance by a method of delay compensation, which accounts for observation delay. Through numerical analysis, we show that DSPCLT achieves performance similar or superior to existing algorithms with only a fraction of computational requirement.

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