Noise source localization by applying multiple signal classification with wavelet transformation

2007 ◽  
Vol 221 (1) ◽  
pp. 11-21 ◽  
Author(s):  
B Ko ◽  
S Lee
Keyword(s):  
Source Localization ◽  
Numerical Experiments ◽  
Wavelet Transformation ◽  
Noise Source ◽  
Signal To Noise Ratio ◽  
Signal Classification ◽  
Discrete Wavelet ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal

In an inverse acoustic problem with nearfield sources, it is important to separate multiple acoustic sources and to measure the position of each target. This paper proposes a new algorithm by applying multiple signal classification (MUSIC) to the outputs of discrete wavelet transformation with subband selection based on the entropy threshold. Some numerical experiments show that the proposed method can estimate the more precise positions than a conventional MUSIC algorithm under moderately correlated signal and relatively low signal-to-noise ratio case.

A Dynamic Sub-Array Divided Technique for Spherical Conformal Array Antenna Using K-Means Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 2884-2888 ◽  
Author(s):  
Jin Yan Tang ◽  
Yue Lei Xie ◽  
Cheng Cheng Peng
Keyword(s):  
Direction Of Arrival ◽  
Array Antenna ◽  
Signal Classification ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Conformal Array ◽  
Multiple Signal

In this paper, a sub-array divided technique using K-means algorithm for spherical conformal array is proposed. All elements of spherical conformal array can be divided into a few sub-arrays by employing the K-means algorithm, and the standard multiple signal classification (MUSIC) algorithm is applied to estimate signals Direction-of-arrival (DOA) on these sub-arrays. Simulations of estimating DOA on a rotational spherical conformal array have been made and the results show that the resolution of DOA is improved by our method compare to existing methods.

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.

MULTIPLE SIGNAL CLASSIFICATION FOR GRAVITATIONAL WAVE BURST SEARCH

2013 ◽  
Vol 23 ◽  
pp. 74-81
Author(s):  
JUNWEI CAO ◽  
ZHENGQI HE
Keyword(s):  
Gravitational Wave ◽  
Doa Estimation ◽  
Signal Classification ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Wave Characteristics ◽  
Multiple Signal ◽  
Promising Tool ◽  
Arbitrary Position ◽  
Noise Covariance

This work is mainly focused on the application of the multiple signal classification (MUSIC) algorithm for gravitational wave burst search. This algorithm extracts important gravitational wave characteristics from signals coming from detectors with arbitrary position, orientation and noise covariance. In this paper, the MUSIC algorithm is described in detail along with the necessary adjustments required for gravitational wave burst search. The algorithm's performance is measured using simulated signals and noise. MUSIC is compared with the Q-transform for signal triggering and with Bayesian analysis for direction of arrival (DOA) estimation, using the Ω-pipeline. Experimental results show that MUSIC has a lower resolution but is faster. MUSIC is a promising tool for real-time gravitational wave search for multi-messenger astronomy.

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.

scholarly journals Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 136
Author(s):  
Pan Gong ◽  
Xixin Chen
Keyword(s):  
Covariance Matrix ◽  
Lower Boundary ◽  
Direction Of Arrival ◽  
Doa Estimation ◽  
Mimo Radar ◽  
Signal Classification ◽  
Computationally Efficient ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal

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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