• 대한기계학회논문집A
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• 제29권1호
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• pp.59-66
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• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• 정보과학회 논문지
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• 제42권2호
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• pp.190-202
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• 2015

스마트폰내 GPS 및 다양한 센서 데이터를 이용하여 스마트폰 사용자의 이동 패턴을 학습하고, 이를 기반으로 사용자 목적지와 경로를 예측하여 사용자의 의도에 맞는 서비스를 제공하는 위치기반 지능형 개인화 서비스(Intelligent personal assistant) 연구가 활발히 진행 되고 있다. 위치기반 개인화 서비스의 지능성은 불완전한 센서 데이터로부터 사용자 이동 정보를 처리하여, 실시간으로 사용자의 경로를 예측하는 정확성과 효율성에 좌우된다. 본 논문은 불완전한 정보로부터 사용자의 경로와 목적지를 추론하는 동적 베이지안 네트워크 기반의 강인한 파티클 필터(Robust particle filter)를 제안한다. 제안한 강인한 파티클 필터 방법은 부정확하고, 불완전한 센서 정보를 보완할 수 있는 파티클 생성, 실시간에 계산 복잡도를 감소시키는 효율적인 스위칭 함수와 가중치 함수, 파티클의 정확도를 향상시키는 재표본화로 구성되며, 사용자의 목적지와 경로의 예측 정확성과 효율성의 성능을 향상시켰다.

• 제어로봇시스템학회논문지
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• 제14권9호
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• pp.931-938
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• 2008

In this paper, a novel localization algorithm robust to the unmodeled systematic odometry errors is proposed for low-cost non-holonomic mobile robots. It is well known that the most pose estimators using odometry measurements cannot avoid the performance degradation due to the dead-reckoning of systematic odometry errors. As a remedy for this problem, we tty to reflect the wheelbase error in the robot motion model as a parametric uncertainty. Applying the Krein space estimation theory for the discrete-time uncertain nonlinear motion model results in the extended robust Kalman filter. This idea comes from the fact that systematic odometry errors might be regarded as the parametric uncertainties satisfying the sum quadratic constrains (SQCs). The advantage of the proposed methodology is that it has the same recursive structure as the conventional extended Kalman filter, which makes our scheme suitable for real-time applications. Moreover, it guarantees the satisfactoty localization performance even in the presence of wheelbase uncertainty which is hard to model or estimate but often arises from real driving environments. The computer simulations will be given to demonstrate the robustness of the suggested localization algorithm.

• 정보처리학회논문지C
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• 제9C권4호
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• pp.459-466
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• 2002

본 논문에서는 웨이블렛 서브밴드 필터링기법을 이용하여 다중의사 결정기법에 기반을 둔 잡음에 강인한 화자식별 알고리즘을 제안한다. 제안된 방법은 잡음이 첨가된 음성신호를 웨이블렛 서브밴드 필터뱅크를 이용하여 각 주파수 대역별로 신호를 분리한 후 개별적인 대역별로 인식 알고리즘을 수행하기 때문에 어떤 서브밴드에서의 노이즈 영향이 상대적으로 적으므로 대역제약된 형태로 주어지는 일반적인 주변잡음이 있는 환경하에서 우수한 성능을 보일 수 있도록 시스템을 구성하였다. 제안된 알고리즘은 화자인식 기법으로 널리 쓰이고 있는 벡터양자화 알고리즘만을 적용한 경우에 비해 15∼60%의 향상된 인식률을 보였다.

• 전기학회논문지P
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• 제65권3호
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• pp.171-177
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• 2016

Noise problem that occurs in living environment is a big trouble in the economic, social and environmental aspects. In this paper, the filtered-X LMS algorithms, the adjoint LMS algorithms, and the robust adjoint LMS algorithms will be introduced for applications in active noise control(ANC). The filtered-X LMS algorithms is currently the most popular method for adapting a filter when the filter exits a transfer function in the error path. The adjoint LMS algorithms, that prefilter the error signals instead of divided reference signals in frequency band, is also used for adaptive filter algorithms to reduce the computational burden of multi-channel ANC systems such as the 3D space. To improve performance of the adjoint LMS ANC system, an off-line measured transfer function is connected parallel to the LMS filter. This parallel-fixed filter acts as a noise controller only when the LMS filter is abnormal condition. The superior performance of the proposed system was compared through simulation with the adjoint LMS ANC system when the adaptive filter is in normal and abnormal condition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제5권7호
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• pp.1214-1229
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• 2011

Power control is a key technique in spectrum underlay cognitive network to guarantee the interference temperature limit of the primary users (PUs) and the quality of service of the secondary users (SUs). In this paper, a robust power control scheme via link gain pricing with $H_{\infty}$ estimator is proposed. The scheme guarantees the interference temperature of the PUs through operating in the network-centric manner, and keeps the fairness between the SUs through link gain pricing. Furthermore, the $H_{\infty}$ filter is also used in the proposed scheme to estimate the channel variation, and thus the power control scheme is robust to the severe channel fading. Plenty of simulations are taken, and prove its superior robust performance against the channel fading, and its effectiveness in guaranteeing the interference temperature limit of the PUs.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.177.3-177
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• 2001

In this paper, the design of the in-motion alignment system of Strapdown Inertial Navigation System(SDINS) using Robust Extended Kalman Filter(REKF) is presented. The compensation of errors in the aided navigation system is accomplished by the indirect feedback filtering. The performance of the aided navigation algorithm is very sensitive to the accuracy of the initial estimate, which is the characteristic of the EKF. Unfortunately, the initial attitude error can be very large during the in-motion alignment. To overcome the in-motion alignment under large initial attitude error problem, the REKF using linear robust filtering technique is proposed. The linear robust H$_2$ filter can be adopted for nonlinear ...

• 전기학회논문지
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• 제58권9호
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• pp.1796-1801
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• 2009

This paper is concerned with the problem of delay-dependent robust $H_{\infty}$ filtering for uncertain descriptor systems with time-varying delay. The considering uncertainty is convex compact set of polytoic type. The purpose is the design of a linear filter such that the resulting filtering error descriptor system is regular, impulse-free, and asymptotically stable with $H_{\infty}$ norm bound. By establishing a finite sum inequality based on quadratic terms, a new delay-dependent bounded real lemma (BRL) for delayed descriptor systems is derived. Based on the derived BRL, a robust $H_{\infty}$ filter is designed in terms of linear matrix inequaltity (LMI). Numerical examples are given to illustrate the effectiveness of the proposed method.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.177-178
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• 2008

An LMI-based method for robust $L_2-L_{\infty}$ filter design is proposed for poly topic uncertain time-delay systems. By using the Projection Lemma and a suitable linearizing transformation, a strict LMI condition for $L_2-L_{\infty}$ filter design is obtained, which does not involve any iterations for design-parameter search, any couplings between the Lyapunov and system matrices, nor any system-dependent filter parameterization. Therefore, the proposed condition enables one to easily adopt, with help of efficient numerical solvers, a parameter-dependent Lyapunov function approach for reducing conservatism, and to design both robust and parameter-dependent filters for uncertain and parameter-dependent time-delay systems, respectively.

• 전자공학회논문지SC
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• 제48권5호
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• pp.59-65
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• 2011

본 논문에서는 폴리토픽 불확실성을 가지는 이산시간 변수종속 특이시스템에 대한 저차(low order)의 변수종속 차수축소 강인 $H_{\infty}$ 필터 설계기법을 제안한다. 먼저, 변수종속 특이시스템에 대한 유계 실수정리(bounded real lemma)를 변수종속 리아푸노프 (Lyapunov) 함수로부터 유도한다. 유계 실수정리로부터 폴리토픽 기법과 새로운 차수축소 기법을 이용하여 저차의 강인 $H_{\infty}$ 필터 설계방법을 볼록최적화가 가능한 선형행렬부등식 접근방법을 이용하여 제시한다. 따라서 제안하는 변수종속 차수축소 강인 $H_{\infty}$ 필터 설계방법은 미리 정한 차수의 $H_{\infty}$ 필터를 제공한다. 수치예제를 통하여 제시한 저차의 필터 설계방법의 타당성을 보인다.