• Smart Structures and Systems
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• v.11 no.1
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• pp.19-34
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• 2013

The objective of this paper is to develop on-line system parameter estimation and damage detection technique from the response measurements through using the Recursive Covariance-Driven Stochastic Subspace identification (RSSI-COV) approach. To reduce the effect of noise on the results of identification, discussion on the pre-processing of data using recursive singular spectrum analysis (rSSA) is presented to remove the noise contaminant measurements so as to enhance the stability of data analysis. Through the application of rSSA-SSI-COV to the vibration measurement of bridge during scouring experiment, the ability of the proposed algorithm was proved to be robust to the noise perturbations and offers a very good online tracking capability. The accuracy and robustness offered by rSSA-SSI-COV provides a key to obtain the evidence of imminent bridge settlement and a very stable modal frequency tracking which makes it possible for early warning. The peak values of the identified $1^{st}$ mode shape slope ratio has shown to be a good indicator for damage location, meanwhile, the drastic movements of the peak of $2^{nd}$ mode slope ratio could be used as another feature to indicate imminent pier settlement.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.325-328
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• 2004

본 논문에서는 PAST(Projection Approximation Subspace Tracking)에 기반 한 새로운 부공간(subspace) 추적 알고리즘을 제안하고자 한다. 빠른 시분할 대상의 목표물의 방위각을 추정하는 것이 필요하다. 그러나 PAST 기법은 고속의 시분할 환경에서는 잘 동작하지 않는다 따라서 가변망각 인자를 도입하여 빠르게 변화하는 부공간의 비정재 (Nonstationary) 상태에 잘 적응시켜 PAST 성능 향상을 보고자 한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.3
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• pp.267-274
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• 2009

Recently, UAVs (Unmanned Aerial Vehicles) are expected much as the Unmanned Systems for various missions. These missions are often based on the Vision System. Especially, missions such as surveillance and pursuit have a process which is carried on through the transmitted vision data from the UAV. In case of small UAVs, monocular vision is often used to consider weights and expenses. Research of missions performance using the monocular vision is continued but, actually, ground and target model have difference in distance from the UAV. So, 3D distance measurement is still incorrect. In this study, Mean-Shift Algorithm, Optical Flow and Subspace Method are posed to estimate the relative depth. Mean-Shift Algorithm is used for target tracking and determining Region of Interest (ROI). Optical Flow includes image motion information using pixel intensity. After that, Subspace Method computes the translation and rotation of image and estimates the relative depth. Finally, we present the results of this study using images obtained from the UAV experiments.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11B
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• pp.1510-1521
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• 2001

In this paper a subspace-based blind interference cancellation is proposed and its performance is analyzed. Then the blind adaptive implementation is devolped using the improved natural power method which is the signal subspace tracking algorithm. The theoretical analysis shows that when the exact covariance matrix is kown the performance of the proposed detector is the same as that of the decorrelating detector. And when the covariance matrix is estimated the asymptotic results are examined. The results of computer simulation demonstrate that the proposed detector outperforms the previous blind adaptive RLS MOE detector.

• Journal of the Korean Society of Industry Convergence
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• v.6 no.3
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• pp.177-183
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• 2003

Recently, Ryu et al. proposed a multiple target DOA tracking algorithm, which has good features that it has no data association problem and simple structure. But its performance is seriously degraded in the low signal-to-noise ratio. In this paper, a measurement fusion method is presented based on ML(Maximum Likelihood), and the new DOA tracking algorithm is proposed by incorporating the presented fusion method into Ryu's algorithm. The proposed algorithm has a better tracking performance than that of Ryu's algorithm, and it sustains the good features of Ryu's algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.37-40
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• 2000

기존의 신호 공간 추적 방법을 이용한 blind multiuser detector는 nonstationary 환경에서 새로운 환경에 적응하기 위해 비교적 긴 시간을 필요로 한다 본 논문은 가변 망각 인자를 도입한 확장 PASTd (Projection Approximation Subspace Tracking with Deflation) 알고리즘을 이용하여 환경 변화에 좀더 신속히 적응하는 성능 향상을 모의실험을 통해 보이고자 한다.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.6
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• pp.1-8
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• 2001

Recently, Ryu et al. proposed a multiple target angle tracking algorithm using the angular innovation extracted from the estimated signal subspace. This algorithm obtains the angles of targets and associates data simultaneously. Therefore, it has a simple structure without data association problem. However it requires the calculation of the inverse of a real matrix with dimension (2N+1)${\times}$(2N+1) to obtain the angular innovations of N targets. In this paper, a new linear equation for angular innovation is proposed using the fact that the projection error is zero when the target steering vector is projected onto the signal subspace. As a result, the proposed algorithm dose not require the matrix inversion and is computationally efficient.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4
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• pp.230-237
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• 2005

The angle-tracking of maneuvering targets is required to the state estimation and classification of targets in underwater acoustic systems. The Problem of angle-tracking multiple closed and crossing targets has been studied by various authors. Sword et al. Proposed a multiple target an91e-tracking algorithm using angular innovations of the targets during a sampling Period are estimated in the least square sense using the most recent estimate of the sensor output covariance matrix. This algorithm has attractive features of simple structure and avoidance of data association problem. Ryu et al. recently Proposed an effective multiple target angle-tracking algorithm which can obtain the angular innovations of the targets from a signal subspace instead of the sensor output covariance matrix. Hwang et al. improved the computational performance of a multiple target angle-tracking algorithm based on the fact that the steering vector and the noise subspace are orthogonal. These algorithms. however. are ineffective when a subset of the incident sources are coherent. In this Paper, we proposed a new multiple target angle-tracking algorithm for coherent and incoherent sources. The proposed algorithm uses the relationship between source steering vectors and the signal eigenvectors which are multiplied noise covariance matrix. The computer simulation results demonstrate the improved Performance of the Proposed algorithm.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).