• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1302-1309
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• 2014

In this paper, we propose a method to improve the performance of the direction-of-arrival (DOA) estimation of a speech source using a multiple signal classification (MUSIC)-based algorithm. Basically, the proposed algorithm utilizes a complex coefficient band pass filter to generate the narrow band signals for signal analysis. Also, reverberation component reduction and quadratic function-based response approximation in MUSIC spatial spectrum are utilized to improve the accuracy of DOA estimation. Experimental results show that the proposed method outperforms the well-known generalized cross-correlation (GCC)-based DOA estimation algorithm in the aspect of the estimation error and success rate, respectively.Abstract should be placed here. These instructions give you guidelines for preparing papers for JICCE.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.193-206
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• 2022

Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks' sizes.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.3
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• pp.294-303
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• 2008

ISAR(Inverse Synthetic Aperture Radar) images represent the 2-D(two-dimensional) spatial distribution of RCS (Radar Cross Section) of an object, and they can be applied to the problem of target identification. A traditional approach to ISAR imaging is to use a 2-D IFFT(Inverse Fast Fourier Transform). However, the 2-D IFFT results in low resolution ISAR images especially when the measured frequency bandwidth and angular region are limited. In order to improve the resolution capability of the Fourier transform, various high-resolution spectral estimation approaches have been applied to obtain ISAR images, such as AR(Auto Regressive), MUSIC(Multiple Signal Classification) or Modified MUSIC algorithms. In this study, these high-resolution spectral estimators as well as 2-D IFFT approach are combined with a recently developed ISAR image classification algorithm, and their performances are carefully analyzed and compared in the framework of radar target recognition.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1081-1088
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• 2021

The modern wireless communication system employes the beamforming technique based on a massive array antenna with a number of elements, for supporting the smooth communication services. A reliable beamforming technology requires the Angle-of-Arrival(: AOA) information for the signal incident to the receiving antenna, which is generally estimated by the high-resolution AOA estimation algorithm such as Multiple Signal Classification(: MUSIC). Although the MUSIC algorithm has the excellent estimation performance, it is difficult to estimate AOA in real time for the massive array antenna due to the extremely high computational complexity. In order to enhance this problem, in this paper, we propose the cascade AOA estimation algorithm based on a Flexible Massive Concentric Circular Array(: FMCCA) antenna with the On/Off function for antenna elements. The proposed cascade algorithm consists of the CAPON algorithm using some elements among entire antenna elements and the Beamspace MUSIC algorithm using entire elements. We provide computer simulation results for various scenarios to demonstrate the AOA estimation performance of the proposed approach.

• Journal of the Institute of Convergence Signal Processing
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• v.7 no.4
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• pp.189-194
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• 2006

In this paper, design of MUSIC algorithm, which is one of high resolution DOA (direction of arrival) estimation techniques was studied. Generally the complex-valued correlation matrix of MUSIC algorithm is transformed to unitary matrix or matrix expansion for the real hardware implementation. Using the orthogonality between the noise subspace eigenvectors and the steering vectors corresponding to signal component, we estimate DOA with the real-valued computation between steering vectors and noise subspace eigenvectors. The DOA algorithm was designed with VHDL models with considerations of 2 elements and 1 incident wave and its simulation results are derived.

• Journal of Biomedical Engineering Research
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• v.31 no.6
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• pp.456-463
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• 2010

In the present study, we proposed a new subspace scanning algorithm to enhance the spatial resolution of electroencephalography (EEG) and magnetoencephalography(MEG) source localization. Subspace scanning algorithms, represented by the multiple signal classification (MUSIC) algorithm and the first principal vector (FINE) algorithm, have been widely used to localize asynchronous multiple dipolar sources in human cerebral cortex. The conventional MUSIC algorithm used principal component analysis (PCA) to extract the noise vector subspace, thereby having difficulty in discriminating two or more closely-spaced cortical sources. The FINE algorithm addressed the problem by using only a part of the noise vector subspace, but there was no golden rule to determine the number of noise vectors. In the present work, we estimated a non-orthogonal signal vector set using independent component analysis (ICA) instead of using PCA and performed the source scanning process in the signal vector subspace, not in the noise vector subspace. Realistic 2D and 3D computer simulations, which compared the spatial resolutions of various algorithms under different noise levels, showed that the proposed ICA-MUSIC algorithm has the highest spatial resolution, suggesting that it can be a useful tool for practical EEG/MEG source localization.

• 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 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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.1003-1008
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• 2001

In beamforming method, source positions are predicted by MUSIC (Multiple Signal Classification) power method and composite sound fields can then be decomposed into each partial field by beamforming, detenninistically without restriction of the distance between reference microphones and sources. However, reference microphone array shape is important in both MUSIC and beamforming method. Thus the present paper describes the effect of the reference microphone array shape.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.41-46
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• 2010

In this paper, the DoA(Direction of Arrival) estimator using unitary MUSIC algorithm is studied. The complex-valued correlation matrix of MUSIC algorithm is transformed to the real-valued one using unitary transform for easy implementation. The eigenvalue and eigenvector are obtained by the combined Jacobi-CORDIC algorithm. CORDIC algorithm can be implemented by only ADD and SHIFT operations and MUSIC spectrum computed by 256 point DFT algorithm. Results of unitary MUSIC algorithm designed by System Generator for FPGA implementation is entirely consistent with Matlab results. Its performance is evaluated through hardware co-simulation and resource estimation.