scholarly journals Mixed-Field Source Localization Based on the Non-Hermitian Matrix

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 56
Author(s):  
Minggang Mo ◽  
Zhaowei Sun
Keyword(s):  
Source Localization ◽  
Hermitian Matrix ◽  
High Order ◽  
Signal Classification ◽  
Range Estimation ◽  
Field Source ◽  
Multiple Signal Classification ◽  
Schmidt Orthogonalization ◽  
Simulation Results ◽  
Mixed Field

In this paper, an efficient high-order multiple signal classification (MUSIC)-like method is proposed for mixed-field source localization. Firstly, a non-Hermitian matrix is designed based on a high-order cumulant. One of the steering matrices, that is related only with the directions of arrival (DOA), is proved to be orthogonal with the eigenvectors corresponding to the zero eigenvalues. The other steering matrix that contains the information of both the DOA and range is proved to span the same column subspace with the eigenvectors corresponding to the non-zero eigenvalues. By applying the Gram–Schmidt orthogonalization, the range estimation can be achieved one by one after substituting each estimated DOA. The analysis shows that the computational complexity of the proposed method is lower than other methods, and the effectiveness of the proposed method is shown with some simulation results.

scholarly journals Low-Complexity High-Order Propagator Method for Near-Field Source Localization

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 54
Author(s):  
Jianzhong Li ◽  
Yide Wang ◽  
Cédric Le Bastard ◽  
Zongze Wu ◽  
Shaoyang Men
Keyword(s):  
Hermitian Matrix ◽  
Near Field ◽  
Low Complexity ◽  
High Order ◽  
Field Source ◽  
Propagator Method ◽  
Lower Complexity ◽  
Simulation Results ◽  
Two Parameters ◽  
High Order Statistics

In this paper, an efficient high-order propagator method is proposed to localize near-field sources. We construct a specific non-Hermitian matrix based on the high-order cumulant of the received signals. With its columns and rows, we can obtain two subspaces orthogonal to all the columns of two steering matrices, respectively, with which the estimation of the directions of arrival (DOA) and ranges of near-field sources can be achieved. Different from other methods, the proposed method needs only one matrix for estimating two parameters separately, therefore leading to a smaller computational burden. Simulation results show that the proposed method achieves the same performance as the other high order statistics-based methods with a lower complexity.

scholarly journals Bearing and Range Estimation Algorithm for Buried Object in Underwater Acoustics

2009 ◽  
Vol 2009 ◽  
pp. 1-4
Author(s):  
Dong Han ◽  
Caroline Fossati ◽  
Salah Bourennane ◽  
Zineb Saidi
Keyword(s):  
Underwater Acoustics ◽  
Classical Model ◽  
Acoustic Scattering ◽  
Estimation Algorithm ◽  
Signal Classification ◽  
Music Algorithm ◽  
Range Estimation ◽  
Multiple Signal Classification ◽  
Buried Objects ◽  
Buried Object

A new algorithm which associates (Multiple Signal Classification) MUSIC with acoustic scattering model for bearing and range estimation is proposed. This algorithm takes into account the reflection and the refraction of wave in the interface of water-sediment in underwater acoustics. A new directional vector, which contains the Direction-Of-Arrival (DOA) of objects and objects-sensors distances, is used in MUSIC algorithm instead of classical model. The influence of the depth of buried objects is discussed. Finally, the numerical results are given in the case of buried cylindrical shells.

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.

Impact source localization in plate utilizing multiple signal classification

2012 ◽  
Vol 227 (4) ◽  
pp. 703-713 ◽  
Author(s):  
HongJun Yang ◽  
Young Jun Lee ◽  
Sang Kwon Lee
Keyword(s):  
Time Delay ◽  
Group Velocity ◽  
Source Localization ◽  
Direction Of Arrival ◽  
Signal Classification ◽  
Classification Method ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
The Impact ◽  
Time Delay Of Arrival

This article proposed a multiple signal classification method based on array signal processing for impact source localization in a plate. For source localization, the direction of arrival of the wave caused by an impact on a plate and the distance between the impact position and sensor should be estimated. The direction of arrival can be estimated accurately using the multiple signal classification method; the distance can be obtained using the time delay of arrival and the group velocity of the Lamb wave in a plate. The time delay of arrival is experimentally estimated using the continuous wavelet transform for the wave. The group velocity is theoretically obtained based on the elastodynamic theory.

ITERATIVE MUSIC FOR HIGHLY CORRELATED EEG/MEG SOURCE LOCALIZATION

2013 ◽  
Vol 25 (02) ◽  
pp. 1350019
Author(s):  
Yuan Cui ◽  
Shan Gao ◽  
Junpeng Zhang
Keyword(s):  
Source Localization ◽  
Real Data ◽  
Signal Classification ◽  
Correlated Sources ◽  
Multiple Signal Classification ◽  
True Source ◽  
Eeg Source Localization ◽  
Auditory Cortices ◽  
Highly Correlated ◽  
Computation Load

This study presented an iterative MUSIC (Multiple Signal Classification) for highly correlated EEG source localization. By suppressing the equivalent false source, the approximate true source location information was obtained. And then, by iteratively suppressing source found in the last iteration, eventually, both of the sources were identified. The method is designed to tackle highly correlated sources, for example, bilateral activations at primary auditory/auditory cortices, at which cases conventional MUSIC has difficulty. Compared with other similar methods, the presented one needs less computation load since it utilizes the minor difference between sources, as can be adequately explained by a theoretical model for correlated sources. Simulation and real data test confirmed its effectiveness.

Ambiguity-Function-Based Methods for Near-Field Source Localization

2012 ◽  
Vol 160 ◽  
pp. 395-399
Author(s):  
Xin Sun ◽  
Hong Jiang ◽  
Bo Wang
Keyword(s):  
Source Localization ◽  
Signal To Noise Ratio ◽  
Near Field ◽  
High Accuracy ◽  
Ambiguity Function ◽  
Far Field ◽  
Range Estimation ◽  
Signal To Noise ◽  
Field Source ◽  
Stationary Signals

In this paper, the AD-MUSIC method for far-field source localization is introduced to near-field, a two-dimensional MUSIC method based on ambiguity function distribution is proposed. Further, a novel AD-ROOT-MUSIC method is proposed for near-field source localization which constructes two matrices based on ambiguity function. They both have high accuracy and resolution for the bearing and range estimation of non-stationary signals in the near field, even when the signal-to-noise ratio (SNR) is low. The effectivity of the methods are validated by simulations.

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