Multiple Signal Classification (MUSIC) Algorithm Hosted On The High Speed Systolic Array Processor (HISSAP)

1989 ◽  
Author(s):  
Joseph P. Loughlin
Keyword(s):  
Systolic Array ◽  
High Speed ◽  
Signal Classification ◽  
Array Processor ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal

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.

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.

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.

scholarly journals Wireless sensor network’s localization based on multiple signal classification algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 498
Author(s):  
Nabeel Aad Lafta ◽  
Saad S. Hreshee
Keyword(s):  
Physical Phenomenon ◽  
Base Station ◽  
Signal Classification ◽  
Wireless Sensor ◽  
Natural Phenomena ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
Active Sensor ◽  
Matlab Program

Wireless sensor networks (WSNs) are a number of sensitive nodes senses a physical phenomenon at the position of their deployment then sends information to the base station to take appropriate operation. (WSNs) are used in many applications such track military targets, discover fires, study natural phenomena such as earthquakes, humidity, heat, etc. The nodes are spread in large areas and it is difficult to locate them manually because they are published randomly by planes or any other method and since the information received from sensitive nodes is useless without knowing their location in this case a problem resulted in the positioning of the nodes. So it unacceptable to equip each sensor node with global position system (GPS) due to various problems such as raises cost and energy consumption. In this paper explained a non-GPS technique to self-positioning of nodes in (WSNs) by using the multiple signal classification (MUSIC) algorithm to determine the position of the active sensor through estimated the direction of arrival (DOA) of the node signal. Then modified MUSIC algorithm (M-MUSIC) to solve the problem of coherent signal. MATLAB program successfully used to simulate the proposed algorithm.

scholarly journals Multiple Signal Classification-Based Impact Localization in Composite Structures Using Optimized Ensemble Empirical Mode Decomposition

2018 ◽  
Vol 8 (9) ◽  
pp. 1447 ◽  
Author(s):  
Yongteng Zhong ◽  
Jiawei Xiang ◽  
Xiaoyu Chen ◽  
Yongying Jiang ◽  
Jihong Pang
Keyword(s):  
Composite Structures ◽  
Sensor Array ◽  
Ensemble Empirical Mode Decomposition ◽  
Signal Classification ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
Mode Decomposition ◽  
The Impact ◽  
Impact Localization

Multiple signal classification (MUSIC) algorithm-based structural health monitoring technology is a promising method because of its directional scanning ability and easy arrangement of the sensor array. However, in previous MUSIC-based impact location methods, the narrowband signals at a particular central frequency had to be extracted from the wideband Lamb waves induced by each impact using a wavelet transform. Additionally, the specific center frequency had to be obtained after carefully analyzing the impact signal, which is time consuming. Aiming at solving this problem, this paper presents an improved approach that combines the optimized ensemble empirical mode decomposition (EEMD) and two-dimensional multiple signal classification (2D-MUSIC) algorithm for real-time impact localization on composite structures. Firstly, the impact signal at an unknown position is obtained using a unified linear sensor array. Secondly, the fast Hilbert Huang transform (HHT) with an optimized EEMD algorithm is introduced to extract intrinsic mode functions (IMFs) from impact signals. Then, all IMFs in the whole frequency domain are directly used as the input vector of the 2D-MUSIC model separately to locate the impact source. Experimental data collected from a cross-ply glass fiber reinforced composite plate are used to validate the proposed approach. The results show that the use of optimized EEMD and 2D-MUSIC is suitable for real-time impact localization of composite structures.

scholarly journals Interpretation of MUSIC for Location Detecting of Small Inhomogeneities Surrounded by Random Scatterers

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Won-Kwang Park
Keyword(s):  
Bessel Function ◽  
Zero Order ◽  
Signal Classification ◽  
Music Algorithm ◽  
Multiple Signal Classification ◽  
Multiple Signal ◽  
Order Bessel Function

We consider the MUltiple SIgnal Classification (MUSIC) algorithm for identifying the locations of small electromagnetic inhomogeneities surrounded by random scatterers. For this purpose, we rigorously analyze the structure of MUSIC-type imaging function by establishing a relationship with zero-order Bessel function of the first kind. This relationship shows certain properties of the MUSIC algorithm, explains some unexplained phenomena, and provides a method for improvements.

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.

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