• 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.

• 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.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.1-8
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• 1992

In the field of prosthesis arm control, the pattern classification of the EMG signal is a required basis process and also the estimation of force from collected EMG data is another necessary duty. But unfortunately, what we've got is not real force but an EMG signal which contains the information of force. This is the reason why we estimate the force from the EMG data. In this paper, when we handle the EMG signal to estimate the force, spatial prewhitening process is applied from which the spatial correlation between the channels are removed. And after the orthogonal transformation which is used in the force estimation process, the transformed signal Is inputed into the probabilistic model for pattern classification. To verify the different results of the multiple channels, SNR(signal to noise ratio) function is introduced.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.777-782
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• 2017

The Signal Intelligence (SIGINT) system based on the adaptive beamformer, comprised of the AOA estimator followed by the interference canceller, is a cutting edge technology for collecting various signal information utilizing all sorts of devices such as the radar and satellite. In this paper, we present the efficient adaptive SIGINT structure consisted of an AOA estimator and an adaptive beamformer. For estimating AOA information of various signals, we employ the Multiple Signal Classification (MUSIC) algorithm and for efficiently suppressing high-power interference signals, we employ the Minimum Variance Distortionless Response (MVDR) algorithm. Also, we provide computer simulation examples to verify the performance of the presented adaptive beamformer structure.

• 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 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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.393-396
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• 1995

In this syudy, the researches classifying the artificial and natural flaws in welding parts are performed using the smart pattern recognition technology. For this purpose the smart signal pattern recognition package including the user defined function was developed and the total procedure including the digital signal processing,feature extraction , feature selection and classifier selection is treated by bulk. Specially it is composed with and discussed using the statistical classifier such as the linear disciminant function classifier, the empirical Bayesian classifier. Also, the smart pattern recognition technology is applied to classification problem of natural flaw(i.e multiple classification problem-crack,lack of penetration,lack of fusion,porosity,and slag inclusion, the planar and volumetric flaw classification problem). According to this results, if appropriately learned the neural network classifier is better than ststistical classifier in the classification problem of natural flaw. And it is possible to acquire the recognition rate of 80% above through it is different a little according to domain extracting the feature and the classifier.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.9-18
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• 2021

The 5G communication system employs the millimeter wave with the extremely high frequency. Since the high frequency signal has the strong straightness, the beamforming technology based on the multiple base stations is required for services covering wide range. The beamformer needs the angle-of-arrival(AOA) information of the signal incident to the antenna, and it is generally estimated through the high resolution AOA estimation algorithm such as Multiple Signal Classification (MUSIC) or Estimation of Signal Parameters via Rotational Invariacne Technique (ESPRIT). Although various antenna array shapes can be employed for the beamformer, a single shape (square, circle, or hexagonal) is typically utilized. In this paper, we introduce a transmitting/receiving beamforming system based on the combined array antenna with square and circular shapes, which is proper to various frequency signals, and evaluate its performance. For evaluating the performance of the proposed beamforming system based on the combined array antenna, we implement the computer simulation employing various scenarios.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.4
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• pp.367-378
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• 2020

This paper introduces the development of the iterative multiple signal classification (MUSIC)-based direction-of-arrival (DOA) estimation system using a rotator that can control the direction of antenna for the global navigation satellite system (GNSS) interference. The system calculates the spatial spectrum according to the noise eigenvector of all dimensions to measure the number of signals (NOS). Also, to detect the false peak, the system adjusts the array antenna's direction and checks the change's peak angles. The phase delay and gain correction values for system calibration are calculated in consideration of the chamber's structure and the characteristics of radio waves. The developed system estimated DOAs of interferences located about 1km away. The field test results show that the developed system can estimate the DOA without NOS information and detect the false peak even though the inter-element spacing is longer than the half-wavelength of the interference.