• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.103-110
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• 2012

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. Using the inverse of the eigenvalue-weighted eigen spectra, the algorithm detects the DOAs of multiple sources. To apply the algorithm in target signal detection for GSC-based beamforming, we utilize its spectral response for the DOA of the target source in noisy conditions. The performance of the proposed target signal detection method is compared with those of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics (ROC) curves.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.2
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• pp.18-28
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• 2008

In 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 MUSIC(Multiple Signal Classification) to the outputs of discrete wavelet transformation with sub-band 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.

• Journal of IKEEE
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• v.24 no.1
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• pp.339-346
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• 2020

In this paper, we propose an angle estimator that is designed to be applied to Bluetooth low-power application technology based on multiple signal classification (MUSIC) algorithm, and present the result of implementation in FPGA. The MUSIC algorithm is designed for H/W high-speed design because it requires a lot of calculations due to high accuracy, and the snapshot variable is designed to cope with various resolution requirements of indoor systems. As a result of the implementation with Xilinx zynq-7000, it was confirmed that 9,081 LUTs were implemented, and it was designed to operate at =the operating frequency of 100MHz.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.89-94
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• 2022

This paper proposes a computationally efficient 2-D direction-of-arrival (DoA) estimation method with a uniform rectangular array (URA). This method is called the direct signal-based method in the sense that it is based directly on the phase relationships among the signals arriving at each antenna of an antenna array rather than their correlation matrix. According to the simulation results, it has be shown that the direct signal-based method, with much less computations than any existing methods, yields the performance comparable to that of the MUSIC (MUltiple SIgnal Classification) method in terms of the root-mean-squared error (RMSE) and the maximum absolute error.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.153-159
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• 2011

The MUSIC (MUltiple SIgnal Classification) method estimates the directions of arrival (DOAs) of the signals impinging on a sensor array based on the fact that the noise subspace is orthogonal to the signal subspace. In the conventional MUSIC, an estimate of the basis for the noise subspace is obtained by eigendecomposing the sample matrix, which is computationally expensive. In this paper, we present a simple DOA estimation method which finds an estimate of the signal subspace basis directly from the columns of the sample matrix from which the noise power components are removed. DOA estimates are obtained by searching for minimum points of a cost function which is defined using the estimated signal subspace basis. The minimum points are efficiently found through the Brent method which employs parabolic interpolation. Simulation shows that the simple estimation method virtually has the same performance as the complex conventional method based on the eigendecomposition.

• The Journal of the Acoustical Society of Korea
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• v.8 no.4
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• pp.44-52
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• 1989

The UCERSS (Unit Circle Eigendecomposition Rational Signal Subspace) algorithm has extended MUSIC (MUltiple Signal Classification ) by using eigendecomposition on the unit circle in order to estimate incident angles of multiple wide band signals. The purpose of this thesis is to further extend the UCERSS to be able to estimate the direction of arrivals of multiple wide band signals in very low SNR . The wide band ESPRIT (Estimation of Signal Parameter via Rotational Invariance Technique) uses covariance difference matrices to reduce noise components. In this paper the wide band ESPRIT which combines the ideas of UCERSS and ESPRIT Is proposed. Computer simulation results Indicate that the performances of the proposed approaches are superior to those of the UCERSS in very low SNR.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.9A
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• pp.1322-1328
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• 1999

In this paper, we propose a Multiple Signal Classification(MUSIC) in conjunction with signal enhancement (SE-MUSIC) for solving the direction-of-arrival estimation problem of multiple incoherent plane waves incident on a uniform linear array. The proposed SE-MUSIC algorithms involve the following main two-step procedure : ( i )to find the enhanced matrix that possesses the prescribed properties and which lies closest to a given covariance matrix estimate in the Frobenius norm sense and (ii) to apply the MUSIC to the enhanced matrix. Simulation results are illustrated to demonstrate the better resolution and statistical performance of the proposed method than MUSIC at lower SNR.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.3
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• pp.252-259
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• 2019

Jet engine modulation(JEM) signals are widely used in the field of target recognition along with high-range resolution profile and inverse synthetic aperture radar because they provide specific information of the jet engine. To obtain the number of blades of the jet engine, the chopping frequency proportional to the number of blades must be extracted. In the conventional chopping frequency extraction method, an initial threshold value is defined and a method of detecting the chopping peak is used. However, this detection method takes time depending on the signal due to repetitive detection. Thus, in this study, we proposed to extract the chopping frequency using MUltiple SIgnal Classification(MUSIC) algorithm. We applied the MUSIC algorithm to a given JEM signal to find the chopping frequency and determine the blade number candidates. We also applied the MUSIC algorithm to other chopping frequency extractions to determine the score of the candidate groups. Unlike the conventional detection algorithm, which requires repetitive frequency detection, MUSIC algorithm quickly detects the accurate chopping frequency and reduces the calculation time.

• Journal of Biomedical Engineering Research
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• v.29 no.4
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• pp.329-336
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• 2008

Recently, causality analysis of source time series extracted from EEG or MEG signals is becoming of great importance in human brain mapping studies and noninvasive diagnosis of various brain diseases. Two approaches have been widely used for the analyses: one is independent component analysis (ICA), and the other is multiple signal classification (MUSIC). To the best of our knowledge, however, any comparison studies to reveal the difference of the two approaches have not been reported. In the present study, we compared the performance of the two different techniques, ICA and MUSIC, especially focusing on how accurately they can estimate and separate various brain electrical signals such as linear, nonlinear, and chaotic signals without a priori knowledge. Results of the realistic simulation studies, adopting directed transfer function (DTF) and Granger causality (GC) as measures of the accurate extraction of source time series, demonstrated that the MUSIC-based approach is more reliable than the ICA-based approach.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.4
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• pp.879-885
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• 2004

This paper describes an adaptive signal processing of digital receiver with digital down convertor(DDC). DDC is composed of numerically controlled oscillator(NCO) and digital low pass filler and the received signal is processed by numerical algorithm. The simulation results of digital receiver using the passband sampling technique are presented and we confirmed that the received low IF signal is converted to zero IF by numerically processed DDC. Direction of arrival(DOA) estimation technique using multiple signal classification(MUSIC) algorithm with high resolution is also discussed. We knew that an accurate resolution of DOA depends on the input sampling numbers and antenna element numbers.