Multiple Signal Direction of Arrival (DoA) Estimation for a Switched-beam System Using Neural Networks

In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer–Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms.

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Direction of Arrival (DOA) Estimation Algorithm Based on the Radial Basis Function Neural Networks

Advances in Intelligent and Soft Computing - Advances in Multimedia, Software Engineering and Computing Vol.1 ◽

10.1007/978-3-642-25989-0_63 ◽

2011 ◽

pp. 389-394 ◽

Cited By ~ 1

Author(s):

Hong He ◽

Tao Li ◽

Tong Yang ◽

Lin He

Keyword(s):

Neural Networks ◽

Radial Basis Function ◽

Basis Function ◽

Direction Of Arrival ◽

Estimation Algorithm ◽

Doa Estimation ◽

Radial Basis

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Direction-of-Arrival Estimation of Electromagnetic Wave Impinging on Spherical Antenna Array in the Presence of Mutual Coupling Using a Multiple Signal Classification Method

Electronics ◽

10.3390/electronics10212651 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2651

Author(s):

Oluwole John Famoriji ◽

Thokozani Shongwe

Keyword(s):

Electromagnetic Wave ◽

Fourier Series ◽

Antenna Array ◽

Mutual Coupling ◽

Direction Of Arrival ◽

Doa Estimation ◽

Signal Classification ◽

Multiple Signal Classification ◽

Multiple Signal ◽

Spherical Antenna

A spherical antenna array (SAA) is the configuration of choice in obtaining an antenna array with isotropic characteristics. An SAA has the capacity to receive an electromagnetic wave (EM) with equal intensity irrespective of the direction-of-arrival (DoA) and polarization. Therefore, the DoA estimation of electromagnetic (EM) waves impinging on an SAA with unknown mutual coupling needs to be considered. In the spherical domain, the traditional multiple signal classification algorithm (SH-MUSIC) is faced with a computational complexity problem. This paper presents a one-dimensional MUSIC method (1D-MUSIC) for the estimation of the azimuth and elevation angles. An intermediate mapping matrix that exists between Fourier series and the spherical harmonic function is designed, and the Fourier series Vandermonde structure is used for the realization of the polynomial rooting technique. This mapping matrix can be computed prior to the DoA estimation, and it is only a function of the array configuration. Based on the mapping matrix, the 2-D angle search is transformed into two 1-D angle findings. Employing the features of the Fourier series, two root polynomials are designed for the estimation of the elevation and azimuth angles, spontaneously. The developed method avoids the 2-D spectral search, and angles are paired in automation. Both numerical simulation results, and results from experimental measured data (i.e., with mutual coupling effect incorporated), show the validity, potency, and potential practical application of the developed algorithm.

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Direction of Arrival Estimation with Microphone Arrays Using SRP-PHAT and Neural Networks

Jornada de Jóvenes Investigadores del I3A ◽

10.26754/jji-i3a.201802824 ◽

2018 ◽

Vol 6 ◽

Author(s):

David Díaz-Guerra Aparicio ◽

José Ramón Beltrán Blázquez

Keyword(s):

Neural Networks ◽

Computational Complexity ◽

Direction Of Arrival ◽

Doa Estimation ◽

Search Space ◽

Microphone Arrays ◽

Direction Of Arrival Estimation ◽

Low Resolution ◽

Phase Transform ◽

Power Maps

The Steered Response Power with phase transform (SRP-PHAT) is one of the most employed techniques for Direction of Arrival (DOA) estimation with microphone arrays, but its computational complexity grows when the search space increases. To solve this issue, we propose the use of Neural Networks (NN) to obtain the DOA from low-resolution SRP-PHAT power maps.

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Direction of Arrival Estimation Under Near-Field Interference Using Matrix Filter

Journal of Computational Acoustics ◽

10.1142/s0218396x1540007x ◽

2015 ◽

Vol 23 (04) ◽

pp. 1540007 ◽

Cited By ~ 3

Author(s):

Guolong Liang ◽

Wenbin Zhao ◽

Zhan Fan

Keyword(s):

Near Field ◽

Direction Of Arrival ◽

Doa Estimation ◽

Direction Of Arrival Estimation ◽

Far Field ◽

Data Simulation ◽

Estimation Algorithms ◽

Simulation Results

Direction of arrival (DOA) estimation is of great interest due to its wide applications in sonar, radar and many other areas. However, the near-field interference is always presented in the received data, which may result in degradation of DOA estimation. An approach which can suppress the near-field interference and preserve the far-field signal desired by using a spatial matrix filter is proposed in this paper and some typical DOA estimation algorithms are adjusted to match the filtered data. Simulation results show that the approach can improve capability of DOA estimation under near-field inference efficiently.

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Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios

Electronics ◽

10.3390/electronics10111236 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1236

Author(s):

Alessandro Cidronali ◽

Edoardo Ciervo ◽

Giovanni Collodi ◽

Stefano Maddio ◽

Marco Passafiume ◽

...

Keyword(s):

Multiple Access ◽

Direction Of Arrival ◽

Doa Estimation ◽

Dual Band ◽

Second Step ◽

Frequency Division ◽

Simulation Environment ◽

Full Wave ◽

Wide Range ◽

Localization Systems

The present paper analyzes the performance of localization systems, based on dual-band Direction of Arrival (DoA) approach, in multi-path affected scenarios. The implemented DoA estimation, which belongs to the so-called Space and Frequency Division Multiple Access (SFDMA) technique, takes advantage of the use of two uncorrelated communication carrier frequencies, as already demonstrated by the authors. Starting from these results, this paper provides, first, the methodology followed to describe the localization system in the proposed simulation environment, and, as a second step, describes how multi-path effects may be taken into account through a set of full-wave simulations. The latter follows an approach based on the two-ray model. The validation of the proposed approach is demonstrated by simulations over a wide range of virtual scenarios. The analysis of the results highlights the ability of the proposed approach to describe multi-path effects and confirms enhancements in DoA estimation as experimentally evaluated by the same authors. To further assess the performance of the aforementioned simulation environment, a comparison between simulated and measured results was carried out, confirming the capability to predict DoA performance.

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DoA Estimation Using Neural Tangent Kernel under Electromagnetic Mutual Coupling

Electronics ◽

10.3390/electronics10091057 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1057

Author(s):

Qifeng Wang ◽

Xiaolin Hu ◽

Xiaobao Deng ◽

Nicholas E. Buris

Keyword(s):

Mutual Coupling ◽

Deep Neural Network ◽

Doa Estimation ◽

Antenna Element ◽

Root Finding ◽

Multiple Signal ◽

Estimation Problems ◽

Limiting Case ◽

Signal Doa Estimation ◽

Polynomial Root Finding

Antenna element mutual coupling degrades the performance of Direction of Arrival (DoA) estimation significantly. In this paper, a novel machine learning-based method via Neural Tangent Kernel (NTK) is employed to address the DoA estimation problem under the effect of electromagnetic mutual coupling. NTK originates from Deep Neural Network (DNN) considerations, based on the limiting case of an infinite number of neurons in each layer, which ultimately leads to very efficient estimators. With the help of the Polynomial Root Finding (PRF) technique, an advanced method, NTK-PRF, is proposed. The method adapts well to multiple-signal scenarios when sources are far apart. Numerical simulations are carried out to demonstrate that this NTK-PRF approach can handle, accurately and very efficiently, multiple-signal DoA estimation problems with realistic mutual coupling.

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