• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1064-1066
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• 2003

In this paper, ground penetrating radar (GPR), which has the capability to detect non metal and plastic mines, is proposed to detect and discriminate antipersonnel (AP) landmines. The time domain GPR - Impulse radar and frequency domain GPR - SFCW (Stepped Frequency Continuous Wave) radar is utilized for metal and non-metal landmine detection and its performance is investigated. Since signal processing is vital for target reorganization and clutter rejection, we implemented the MUSIC (Multiple Signal Classification) algorithm for the signal processing of SFCW radar data and SAR (Synthetic Aperture Radar) processing method for the signal processing of Impulse radar data.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.193-197
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• 2003

Radar target identification can be achieved by using various radar signatures, such as one-dimensional(1-D) range profile, 2-D radar images, and 1-D or 2-D scattering centers on a target. In this letter, five 1-D scattering center extraction methods are discussed - TLS(Total Least Square)-Prony, Fast Root-MUSIC (Multiple Signal Classification), Matrix-Pencil, GEESE(GEneralized Eigenvalues utilizing Signal-subspace Eigenvalues), TLS-ESPRIT(Total Least Squares - Estimation of Signal Parameters via Rotational Invariance Technique), These methods are compared in the context of estimation accuracy as well as a computational efficiency using a noisy data. Finally these methods are applied to the target classification experiment with the measured data in the POSTECH compact range facility.

• 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.

• Journal of Navigation and Port Research
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• v.36 no.10
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• pp.833-838
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• 2012

TDOA which is one of the position estimation methods is used on indoor positioning, jammer localization, rescue of life, etc. due to high accuracy and simple structure. This paper proposes the super-resolution TDOA estimator using MPM(Matrix Pencil Method). The proposed estimator has more accuracy and is applicable to narrowband signal compared with the conventional cross-correlation. Furthermore, its complexity is low because obtained data directly is used for construction of matrix unlike the MUSIC(Multiple Signal Classification) which is one of the well-known super-resolution estimator using covariance matrix. To validate the performance of proposed estimator, errors of estimation and computational burden is compared to MUSIC through software simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.6
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• pp.1130-1141
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• 2015

Conventionally, a pulse radar is used for middle-sized or large-sized ships to detect other ships or obstacles located at a long distance. However, it is hardly equipped for most of the small-sized ships due to mounting and maintenance costs. Therefore, FMCW(frequency modulated continuous wave) radar is suggested as an alternative for the small-sized ships. Since it operates with low power and has good range resolution for relatively close objects, it is eligible for the small-sized ships. In previously proposed FMCW radar system, it only estimates distance and velocity of a target ship placed in the direction of main beam and is hard to detect several ships simultaneously. Thus, we suggest the method for detecting several ships at the same time by applying MUSIC(multiple signal classification) algorithm to FMCW radar signal received by a phased array antenna. In addition, by combining digital beam forming with the MUSIC algorithm, better angle resolution is achievable.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.40 no.1
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• pp.15-23
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• 2003

This paper proposes a fast direction of arrival (DOA) estimation algorithm that can rapidly estimate incidence angles of incoming signals using a pseudo covariance matrix. The conventional subspace DOA estimation methods such as MUSIC (multiple signal classification) algorithms need many sample signals to acquire covariance matrix of input signals. Thus, it is difficult to estimate the DOAs of signals because they cannot perform DOA estimation during receiving sample signals. Also if the D0As of signals are changing rapidly, conventional algorithms cannot estimate incidence angles of signals exactly. The proposed algorithm obtains bearing response and directional spectrum after acquiring pseudo covariance matrix of each snapshot. The incidence angles can be exactly estimated by using the bearing response and directional spectrum. The proposed DOA estimation algorithm uses only concurrent snapshot so as to obtain covariance matrix. Compared to conventional DOA estimation methods. The proposed algorithm has an advantage that can estimate DOA of signal rapidly.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.48-59
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• 2000

Eigendecomposition based direction-of-arrival(DOA) estimation algorithm such as MUSIC(multiple signal classification) is known to perform well and provide high resolution in white noise environment. However, its performance degrades severely when the noise process is not white. In this paper we consider the DOA estimation problem in a colored noise environment as a problem of extracting periodic signals from noise, and we take the problem to the wavelet domain. Covariance matrix of multiscale components which are obtained by taking wavelet decomposition on the noise has a special structure which can be approximated with a banded sparse matrix. Compared with noise the correlation between multiscale components of narrowband signal decays slowly, hence the covariance matrix does not have a banded structure. Based on this fact we propose a DOA estimation algorithm that transforms the covariance matrix into wavelet domain and removes noise components located in specific bands. Simulations have been carried out to analyze the proposed algorithm in colored noise processes with various correlation properties.

• 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 Electrical Engineering and Technology
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• v.13 no.4
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• pp.1614-1622
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• 2018

The research related to fault diagnosis in permanent magnet synchronous motors (PMSMs) has attracted considerable attention in recent years because various faults such as permanent magnet demagnetization and short-circuited turns can occur and result in unexpected failure of motor related system. Several conventional current and back electromotive force (BEMF) analysis techniques were proposed to detect certain faults in PMSMs; however, they generally deal with a single fault only. On the contrary, cases of multiple faults are common in PMSMs. We propose a fault diagnosis method for PMSMs with single and multiple combined faults. Our method uses three phase BEMF voltages based on the fast Fourier transform (FFT), support vector machine(SVM), and visualization tools for identifying fault types and severities in PMSMs. Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) are used to visualize the high-dimensional data into two-dimensional space. Experimental results show good visualization performance and high classification accuracy to identify fault types and severities for single and multiple faults in PMSMs.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.