• 대한전기학회:학술대회논문집
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• 대한전기학회 2011년도 제42회 하계학술대회
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• pp.1738-1739
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• 2011

In this paper, the range difference based the moving target tracking problem using multiple UAVs is solved within the new framework of linear robust state estimation. To do this, the relative kinematics is modeled as an uncertain linear system containing stochastic parametric uncertainties in its measurement matrix. Applying the non-conservative robust Kalman filter for the uncertain system, a quasi-optimal linear target tracking filter is designed. For its recursive linear filter structure, the proposed method can ensure the fast convergence and reliable target tracking performance. Moreover, it is suitable for real-time applications using multiple UAVs.

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.201-207
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• 2006

A robust Kalman filtering method for uncertain stochastic systems is suggested by adopting a perturbation estimation process which is to reconstruct total uncertainty with respect to the nominal state transition equation. The predictor and corrector of discrete Kalman filter are reformulated with the perturbation estimator. Successively, the state and perturbation estimation error dynamics and the corresponding error covariance propagation equations are derived as well. Finally we have the recursive algorithm of Combined Kalman Filter-Perturbation Estimator (CKF). The proposed combined Kalman filter-perturbation estimator has the property of integrating innovations and the adaptation capability to system uncertainties. A numerical example is shown to demonstrate the effectiveness of the proposed scheme.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.759-762
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• 1996

In previous work Kwon and Yoo [5] have shown that the FIR tracking algorithm using the input estimation technique. However, it has not solved the problem of systems with parameter uncertainties. Therefore, in this paper we propose a new robust $H_{\infty}$ FIR tracking filter to solve the target tracking problems under systems with parameter uncertainties. Also, we use here the input estimation approach to account for the possibility of maneuver. Simulation results show that the robust $H_{\infty}$ FIR tracking filter proposed here still has good tracking performance for a maneuvering target tracking problem even under all system parameter uncertainties.

• Journal of Electrical Engineering and Technology
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• 제7권1호
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• pp.132-140
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• 2012

A non-conservative robust Kalman filter (NCRKF) is applied to flight data to identify the aerodynamic derivatives of an unmanned autonomous vehicle (UAV). The NCRKF is formulated using UAV lateral motion data and then compared with results from the conventional Kalman filter (KF) and the recursive least square (RLS) method. A superior performance for the NCRKF is demonstrated by simulation and real flight data. The NCRKF is especially effective in large uncertainties in vehicle modeling and in measuring flight data. Thus, it is expected to be useful in missile and aircraft parameter identification.

• 말소리와 음성과학
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• 제2권4호
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• pp.145-151
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• 2010

In this paper, a robust feature compensation method is proposed for improving the performance of speech recognition. The proposed method is incorporated into the auto-regressive moving average (ARMA) based feature compensation. We employ variable weights for the ARMA filter according to the degree of speech activity, and pass the normalized cepstral sequence through the weighted ARMA filter. Additionally when normalizing the cepstral sequences in training, the cepstral means and variances are estimated from total training utterances. Experimental results show the proposed method significantly improves the speech recognition performance in the noisy and reverberant environments.

• 조명전기설비학회논문지
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• 제29권10호
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• pp.104-110
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• 2015

The disturbance observer (DOB) controller has been widely used in various industrial applications since it is capable of achieving robust stability and disturbance rejection. In this paper, we study the effect of Q-filter on disturbance observer controller for Electro-Magnetic suspension (EMS) systems. We consider three Q-filters and analyze their effects on the robust stability against parameter uncertainties due to mass variation. Moreover, we investigate the influence of sensor noise for three Q-filters. According to our study, robust stability improves as the order of Q-filter decreases. On the other hand, the larger the order of Q-filter, the more the effect of sensor noise can be removed.

• Transactions on Control, Automation and Systems Engineering
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• 제3권2호
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• pp.83-88
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• 2001

The robust linear H(sub)$\infty$ FIR filter, which guarantees a prescribed H(sub)$\infty$ performance, is designed for continuous time-varying systems with unknown cone-bounded nonlinearity. The infinite horizon filtering for time-varying systems is systems is investigated in therms of two Riccati equations by the finite moving horizon.

• 한국음향학회지
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• 제38권1호
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• pp.39-46
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• 2019

본 논문에서는 합성곱 신경망(Convolutional Neural Network, CNN) 구조를 이용하여 잡음 환경에서 음향신호를 분류할 때, 인식률을 높이는 결합 특징을 제안한다. 반면, Wiener filter를 이용한 강인한 log Mel-filter bank와 PNCCs(Power Normalized Cepstral Coefficients)는 CNN 구조의 입력으로 사용되는 2차원 특징을 형성하기 위해 추출됐다. 자연환경에서 43종의 조류 울음소리를 포함한 ebird 데이터베이스는 분류 실험을 위해 사용됐다. 잡음 환경에서 결합 특징의 성능을 평가하기 위해 ebird 데이터베이스를 3종류의 잡음을 이용하여 4개의 다른 SNR (Signal to Noise Ratio)(20 dB, 10 dB, 5 dB, 0 dB)로 합성했다. 결합 특징은 Wiener filter를 적용한 log-Mel filter bank, 적용하지 않은 log-Mel filter bank, 그리고 PNCC와 성능을 비교했다. 결합 특징은 잡음이 없는 환경에서 1.34 % 인식률 향상으로 다른 특징에 비해 높은 성능을 보였다. 추가적으로, 4단계 SNR의 잡음 환경에서 인식률은 shop 잡음 환경과 schoolyard 잡음 환경에서 각각 1.06 %, 0.65 % 향상했다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 D
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• pp.660-662
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• 2000

The most important problem in traget tracking can be said to be modeling the tracking system correctly. Although the simple linear dynamic equation for this model has used until now, the satisfactory performance could not be obtained owing to uncertainties of the real systems in the case of designing the filters based on the dynamic equations. In this paper, we propose the extended robust Kalman filter(ERKF) which can be applied to the real target tracking system with the parameter uncertainties. To solve the robust nonlinear fettering problem, we derive the extended robust Kalman filter equation using the Krein space approach and sum quadratic constraint. We show the proposed filter has better performance than the existing extended Kalman filter(EKF) via 3-dimensional target example.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2265-2267
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• 2002

In this paper, a robust Kalman filter is proposed by introducing a new sliding mode surface. This filter can be used for the system with a matching condition. The new state estimater is designed for stochastic systems with bounded uncertainties.