An Efficient DOA Estimation and Jammer Mitigation Method by Means of a Single Snapshot Compressive Sensing Based Sparse Coprime Array

Ganguly, Saurav; Ghosh, Jayanta; Puli, Kishore Kumar; Mukhopadhyay, Mainak

doi:10.1007/s11277-021-09263-9

Cited by 6 publications

(4 citation statements)

References 39 publications

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“…However, this array configuration suffers from low aperture size of (P − 1) /2, where P is the number of antenna elements and is the wavelength [37]. This reveals that in ULA, increasing the array aperture needed for high resolution requires huge number of antenna elements, which increases the system complexity [27,28]. Nonuniform largespaced antenna arrays can be utilized to acquire higher array aperture than ULA using reduced number of elements [29].…”

Section: Wideband Doa Estimation With Generalized Coprime Arraymentioning

confidence: 99%

“…Several antenna array configurations can be employed for wideband DOA estimation. Uniform linear array (ULA) configuration requires huge number of elements to obtain the large array aperture needed for high resolution, which increases both the hardware/software complexity [27,28]. Nonuniform antenna arrays can be utilized to overcome the ULA drawback of small array aperture and achieve higher array aperture than ULA using reduced number of elements [29,30].…”

Section: Introductionmentioning

confidence: 99%

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A Compressive Sensing Based Computationally Efficient High-Resolution DOA Estimation of Wideband Signals Using Generalized Coprime Arrays

El-Khamy,

El-Shazly,

Eltrass

2024

Wireless Pers Commun

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Most recent state-of-the-art wideband direction of arrival (DOA) estimation techniques achieve reasonable accuracy at the expenses of high computational complexity. In this paper, a new computationally efficient approach based on compressive sensing (CS) is introduced for high resolution wideband DOA estimation. The low software complexity is achieved utilizing CS with deterministic chaotic Chebyshev sensing matrices that allow reducing the measurement vector dimension, while the high-resolution DOA estimation is acquired utilizing an efficient generalized coprime array configuration. The effectiveness of the introduced approach in enhancing the DOA estimation precision and reducing the computational complexity is studied along with a detailed comparison between three state-of-the-art wideband DOA estimation techniques, namely incoherent signal subspace method (ISSM), focusing signal subspace (FSS), and modified test of orthogonality of projected subspaces (mTOPS) with and without applying the proposed CS technique. The performance is examined utilizing various assessment metrics such as the spatial spectrum, the computational time, and the root mean square error between estimated and actual DOAs when varying the signal-to-noise ratio and number of elements. Results reveal that applying the proposed CS technique to the three algorithms (ISSM, FSS, and mTOPS) provides significant reduction in the execution time needed for the DOA estimation without affecting the resolution accuracy under various set of parameters. This reveals the importance of the proposed approach in wideband wireless communication systems.

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Section: Wideband Doa Estimation With Generalized Coprime Arraymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Compressive Sensing Based Computationally Efficient High-Resolution DOA Estimation of Wideband Signals Using Generalized Coprime Arrays

El-Khamy,

El-Shazly,

Eltrass

2024

Wireless Pers Commun

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“…Direction of arrival (DOA) estimation is a critical area in array signal processing and has wide applications in sonar, 5G communication, radar, and smart antennas [1][2][3]. However, most studies on DOA estimation have been performed assuming white Gaussian conditions of ideal additive noise.…”

Section: Introductionmentioning

confidence: 99%

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

Lan,

Hu,

Zhang

et al. 2024

Electronics

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The estimation of direction of arrival (DOA) is paramount in the realm of practical array signal processing systems. Nevertheless, traditional estimation methods often rely heavily on the Gaussian noise assumption, rendering them ineffective in achieving high-precision estimates in environments plagued by strong impulsive noise. To address this challenge, this paper introduces a novel DOA estimation algorithm that leverages mixed fractional lower-order correntropy (MFLOCR) in the context of Alpha-stable distributed impulsive noise. Correntropy is used as a measure of the similarity of the signals, using a Gaussian function to smooth extreme values and provide greater robustness against impulsive noise. By utilizing diverse kernel lengths to jointly regulate the kernel function, the concept of correntropy is expanded and implemented in the fractional lower-order moment (FLOM) algorithm for received signals. Subsequently, the MFLOCR is derived by adjusting the resulting form of correntropy. Finally, an enhanced DOA estimation algorithm is proposed that combines the MFLOCR operator with the MUSIC algorithm, specifically tailored for impulsive noise environments. Furthermore, a proof of boundedness is provided to validate the effectiveness of the proposed approach in such noisy conditions. Simulation experiments confirmed that the proposed method outperforms existing DOA estimation methods in the context of intense impulsive noise, a low generalized signal-to-noise ratio (GSNR), and a smaller number of snapshots.

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“…Subspace-based DOA estimation methods, such as the Multiple Signal Classification (MUSIC) algorithm [5,6] and Estimating Signal Parameters Via Rotational Invariance Techniques (ESPRIT) algorithm [7][8][9], provide high resolution for estimating uncorrelated signals. Then, some algorithms [10][11][12] about compressive sensing are proposed to ensure greater accuracy with a lower number of antennas. However, in the case of coherent signals and impulsive noise, the estimation accuracy of these algorithms decreases due to the rank deficiency of the covariance matrix and noise interference.…”

Section: Introductionmentioning

confidence: 99%

Direction of Arrival Estimation Method Based on Eigenvalues and Eigenvectors for Coherent Signals in Impulsive Noise

Cui,

Pan,

Wang

2024

Mathematics

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In this paper, a Toeplitz construction method based on eigenvalues and eigenvectors is proposed to combine with traditional denoising algorithms, including fractional low-order moment (FLOM), phased fractional low-order moment (PFLOM), and correntropy-based correlation (CRCO) methods. It can improve the direction of arrival (DOA) estimation of signals in impulsive noise. Firstly, the algorithm performs eigenvalue decomposition on the received covariance matrix to obtain eigenvectors and eigenvalues, and then the Toeplitz matrix is created according to the eigenvectors corresponding to its eigenvalues. Secondly, the spatial averaging method is used to obtain an unbiased estimate of the Toeplitz matrix, which is then weighted and added based on the corresponding eigenvalues. Next, the noise subspace of the Toeplitz matrix is reconstructed to obtain the one that has less angle information. Finally, the DOA of the coherent signal is estimated using the Multiple Signal Classification (MUSIC) algorithm. The improved method based on the Toeplitz matrix can not only suppress the effect of impulsive noise but can also solve the problem of aperture loss due to its decoherence. A series of simulations have shown that they have better performances than other algorithms.

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An Efficient DOA Estimation and Jammer Mitigation Method by Means of a Single Snapshot Compressive Sensing Based Sparse Coprime Array

Cited by 6 publications

References 39 publications

A Compressive Sensing Based Computationally Efficient High-Resolution DOA Estimation of Wideband Signals Using Generalized Coprime Arrays

A Compressive Sensing Based Computationally Efficient High-Resolution DOA Estimation of Wideband Signals Using Generalized Coprime Arrays

A Novel DOA Estimation Algorithm Based on Robust Mixed Fractional Lower-Order Correntropy in Impulsive Noise

Direction of Arrival Estimation Method Based on Eigenvalues and Eigenvectors for Coherent Signals in Impulsive Noise

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