The application of high-resolution methods for DOA estimation using a linear antenna array

2014 ◽  
Vol 7 (1) ◽  
pp. 87-94 ◽  
Author(s):  
Lotfi Osman ◽  
Imen Sfar ◽  
Ali Gharsallah
Keyword(s):  
Antenna Array ◽  
Linear Array ◽  
Doa Estimation ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Signal Parameters ◽  
Radio Frequency Signals ◽  
Observation Space ◽  
Signal Covariance Matrix

This paper presents results of direction of arrival (DOA) estimation using multiple signal classification (MUSIC), Root-MUSIC, and estimation of signal parameters via rotational invariance technique algorithms. As is well known, these algorithms are mainly based on the specific properties of the signal covariance matrix as well as the decomposition of the observation space into two subspaces, one for the signal and the other for the noise. Here, we are particularly interested in the quality of sources localization considering only the case of uncorrelated radio frequency signals impinging on an antenna array. A measurement system consisting of a linear array antenna and a five-port network applicable to a demodulator such as a receiver is used for the DOA estimation process. Co-simulations performed with the Advanced Device System and Matlab yielded interesting results not only on their performance but also on their limitation.

scholarly journals A Novel Algorithm to Estimate Closely Spaced Source DOA

2017 ◽  
Vol 7 (4) ◽  
pp. 2109 ◽  
Author(s):  
Sidi Mohamed Hadj Irid ◽  
Samir Kameche ◽  
Said Assous
Keyword(s):  
Classical Music ◽  
Linear Array ◽  
Doa Estimation ◽  
Spatial Sampling ◽  
Signal Classification ◽  
Far Field ◽  
Uniform Linear Array ◽  
Resolution Method ◽  
Multiple Signal Classification ◽  
High Resolution Method

<p>In order to improve resolution and direction of arrival (DOA) estimation of two closely spaced sources, in context of array processing, a new algorithm is presented. However, the proposed algorithm combines both spatial sampling technic to widen the resolution and a high resolution method which is the Multiple Signal Classification (MUSIC) to estimate the DOA of two closely spaced sources impinging on the far-field of Uniform Linear Array (ULA). Simulations examples are discussed to demonstrate the performance and the effectiveness of the proposed approach (referred as Spatial sampling MUSIC SS-MUSIC) compared to the classical MUSIC method when it’s used alone in this context.</p>

scholarly journals 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 ◽  
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.

2D-DOA Estimation for MIMO Radar on Monostatic L-Shaped Minimum-Redundancy Linear Arrays

2014 ◽  
Vol 926-930 ◽  
pp. 2871-2875
Author(s):  
Ying Li ◽  
Gong Zhang
Keyword(s):  
High Efficiency ◽  
Linear Array ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Radar System ◽  
Signal Classification ◽  
Multiple Signal Classification ◽  
Linear Arrays ◽  
2D Doa Estimation ◽  
Better Than

This paper discussed the problem of two dimensional (2D) direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar. The minimum-redundancy linear array (MLRA) is introduced into the transmitting array and receiving array, which enables the high efficiency of the radar system. By utilizing the algorithm of multiple signal classification (MUSIC), we illustrate that the proposed scheme performs better than the uniform linear arrays (ULA) configuration under the same conditions. Simulation results verify the effectiveness of our scheme.

scholarly journals DOA Estimation for Coprime Linear Array Based on MI-ESPRIT and Lookup Table

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3043 ◽  
Author(s):  
Weike Zhang ◽  
Xi Chen ◽  
Kaibo Cui ◽  
Tao Xie ◽  
Naichang Yuan
Keyword(s):  
Linear Array ◽  
Estimation Algorithm ◽  
Doa Estimation ◽  
Spatial Spectrum ◽  
Angle Measurement ◽  
Lookup Table ◽  
Signal Parameters ◽  
Estimation Precision ◽  
Simulation Results ◽  
The Relationship

In order to improve the angle measurement performance of a coprime linear array, this paper proposes a novel direction-of-arrival (DOA) estimation algorithm for a coprime linear array based on the multiple invariance estimation of signal parameters via rotational invariance techniques (MI-ESPRIT) and a lookup table method. The proposed algorithm does not require a spatial spectrum search and uses a lookup table to solve ambiguity, which reduces the computational complexity. To fully use the subarray elements, the DOA estimation precision is higher compared with existing algorithms. Moreover, the algorithm avoids the matching error when multiple signals exist by using the relationship between the signal subspace of two subarrays. Simulation results verify the effectiveness of the proposed algorithm.

Direction of Arrival Estimation with Compact Array Antennas

2009 ◽  
pp. 201-216
Author(s):  
Eddy Taillefer ◽  
Jun Cheng ◽  
Takashi Ohira
Keyword(s):  
Single Port ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Signal Classification ◽  
Direction Of Arrival Estimation ◽  
Multiple Signal Classification ◽  
Array Antennas ◽  
Power Pattern ◽  
Parasitic Array ◽  
Compact Array

This chapter presents direction of arrival (DoA) estimation with a compact array antenna using methods based on reactance switching. The compact array is the single-port electronically steerable parasitic array radiator (Espar) antenna. The antenna beam pattern is controlled though parasitic elements loaded with reactances. DoA estimation using an Espar antenna is proposed with the power pattern cross correlation (PPCC), reactance-domain (RD) multiple signal classification (MUSIC), and, RD estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms. The three methods exploit the reactance diversity provided by an Espar antenna to correlate different antenna output signals measured at different times and for different reactance values. The authors hope that this chapter allows the researchers to appreciate the issues that may be encountered in the implementation of direction-finding application with a single-port compact array like the Espar antenna.

scholarly journals Polarization and DOA Estimation Based on Dual-Polarized Conformal Array

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Chao Liu ◽  
Shuai Xiang ◽  
Liangfeng Xu ◽  
Zhengfei Fang
Keyword(s):  
Doa Estimation ◽  
Signal Classification ◽  
Orthogonal Polarization ◽  
Coherent Signals ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Polarization Signal ◽  
Conformal Array ◽  
Dual Polarized ◽  
Cramer Rao Bound

A dual-polarized multiple signal classification (DP-MUSIC) algorithm is presented to estimate the arrival directions and polarizations for a dual-polarized conformal array. Each polarization signal is decomposed into two orthogonal polarization components, which are considered to be a pair of coherent signals coming from the same direction but different polarization. The polarization parameters are modeled as the equivalent coherence coefficients of the orthogonal polarization components. Then, the method of decoherence can be used to decouple the information of polarization states and signal angles. After that, the direction of arrival (DOA) and polarization parameters can be estimated by the DP-MUSIC algorithm. Moreover, the angles of incident direction are re-estimated, which greatly improves the accuracy of DOA estimation. The Cramer–Rao bound (CRB) is derived and the effectiveness of the proposed algorithm is verified by Monte Carlo simulations.

scholarly journals DOA Estimation of Cylindrical Conformal Array Based on Geometric Algebra

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Minjie Wu ◽  
Xiaofa Zhang ◽  
Jingjian Huang ◽  
Naichang Yuan
Keyword(s):  
Geometric Algebra ◽  
Rotation Angle ◽  
Matrix Representation ◽  
Doa Estimation ◽  
Signal Classification ◽  
Matrix Transformations ◽  
Multiple Signal Classification ◽  
Conformal Array ◽  
Variable Curvature ◽  
Array Structures

Due to the variable curvature of the conformal carrier, the pattern of each element has a different direction. The traditional method of analyzing the conformal array is to use the Euler rotation angle and its matrix representation. However, it is computationally demanding especially for irregular array structures. In this paper, we present a novel algorithm by combining the geometric algebra with Multiple Signal Classification (MUSIC), termed as GA-MUSIC, to solve the direction of arrival (DOA) for cylindrical conformal array. And on this basis, we derive the pattern and array manifold. Compared with the existing algorithms, our proposed one avoids the cumbersome matrix transformations and largely decreases the computational complexity. The simulation results verify the effectiveness of the proposed method.

Sound Source Localization Based on Microphone Uniform Linear Array

2013 ◽  
Vol 748 ◽  
pp. 634-639 ◽  
Author(s):  
Jian Rong Wang ◽  
Ju Zhang ◽  
Song Gun Hyon ◽  
Jian Guo Wei
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Linear Array ◽  
Weighted Average ◽  
Classification Algorithm ◽  
Signal Classification ◽  
Uniform Linear Array ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
The Impact

In order to locate the sound source based on the microphone uniform linear array and reduce the impact of noise and reflection, improved multiple signal classification algorithm and a kind of weighted average filters be used in this paper. According to the microphone array speech processing characteristics, we improved the traditional multiple signal classification algorithm and designed a kind of weighted average filters. Then, we made the computer simulation experiments. The experimental results show that the location of the sound source is the peak with the highest power in the spatial spectrum. Besides, the frequency domain diagram is more smoothly and the power of the noise and reflection is effectively reduced except sound source through the weighted average processing. Therefore, improved multiple signal classification algorithm can achieve sound source localization based on the microphone uniform linear array. And the impact of the noise and reflection is effectively reduced by processing of the weighted average filters.

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