• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.77.3-77
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• 2001

An extended robust H$\infty$ filter is proposed for a nonlinear uncertain system. We also analyze the characteristics of the proposed filter such as an H$\infty$ performance criterion using the Lyapunov function method. The analysis results show that proposed filter has a robustness against disturbances such as process and measurement noises and against parameter uncertainties. Then the in-flight alignment for a strapdown inertial navigation system is designed using the presented filter. Simulation results show that the proposed filter effectively improve the performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.565-569
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• 2003

In this paper, a robust filter is proposed to effectively estimate the system states in the case where system model uncertainties as well as disturbances are present. The proposed robust filter is constructed based on the linear approximation methods for a general nonlinear uncertain system with an integral quadratic constraint. We also derive the important characteristic of the proposed filter, a modified $H_{\infty}$ performance index. Analysis results show that the proposed filter has robustness against disturbances, such as process and measurement noises, and against parameter uncertainties. Simulation results show that the proposed filter effectively improves the performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1961-1963
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• 2001

In a target tracking problem, the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In this study, an extended robust $H_{\infty}$ was developed which can significantly improve the tracking performance when glint noise is present. Through the computer simulations, the proposed filter showed superior and robust tracking performance compared with other extended Kalman filters.

• 전자공학회논문지SC
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• 제48권1호
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• pp.55-60
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• 2011

이동로봇의 동시 위치인식 및 지도작성 (Simultaneous Localization And Mapping, SLAM) 에서 가장 기본이 되는 알고리즘은 확장 칼만 필터 SLAM(Extended Kalman Filter SLAM, EKF-SLAM)이다. 하지만 칼만 필터를 사용할 때, 시스템 설계자는 외부 입력에 대한 시스템적 특성과 외부 노이즈의 확률적 모델을 알고 있어야 하나, 실제 환경에서는 이를 정확히 파악할 수 없는 한계가 있다. 이에, 칼만 필터를 불확실성이 심한 실제 환경에 적용할 경우 내부 변수의 변화에 민감하게 반응하거나, 필터의 수학적 일관성이 지켜지지 않거나 또는 부정확한 상태 변수값을 추정하기도 한다. 이에 비해 $H_{\infty}$ 필터는 외부 모델에 대한 상세한 정보가 없을지라도 강인하게 상태를 예측할 수 있다는 장점을 가지고 있다. 본 논문에서는 이러한 $H_{\infty}$ 필터의 특성이 이용로봇의 SLAM 알고리즘의 성능 향상에 도움이 될 것이라는 아이디어에 착안하여 $H_{\infty}$ 필터에 가번한 SLAM 알고리즘을 제안하고 이를 모의 실험에 적용해 보았다. 이를 통해 불확실성이 큰 환경에서는 제안된 알고리즘이 기존의 EKF-SLAM에 비해 다소 우수한 예측 성능을 보임을 확인할 수 있었다.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1520-1529
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• 2013

In this paper, we propose and assess the performance of "H infinity filter ($H_{\infty}$, HIF)" and "cost reference particle filter (CRPF)" in the problem of tracking a target based on the measurements of the range and the bearing of the target. HIF and CRPF have the common advantageous feature that we do not need to know the noise statistics of the problem in their applications. The performance of the extended Kalman filter (EKF) is also compared with that of the proposed filters, but the noise information is perfectly known for the applications of the EKF. Simulation results show that CRPF outperforms HIF, and is more robust because the tracking of HIF diverges sometimes, particularly when the target track is highly nonlinear. Interestingly, when the tracking of HIF diverges, the tracking of the EKF also tends to deviate significantly from the true track for the same target track. Therefore, CRPF is very effective and appropriate approach to the problems of highly nonlinear model, especially when the noise statistics are unknown. Nonetheless, HIF also can be applied to the problem of timevarying state estimation as the EKF, particularly for the case when the noise statistcs are unknown. This paper provides a good example of how to apply CRPF and HIF to the estimation of dynamically varying and nonlinearly modeled states with unknown noise statistics.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.26-29
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• 2001

In a target tracking problem the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In most nonlinear situations an Extended Robust Kalman Filter(ERKF) can yield acceptable performance as long as the noises are white Gaussian. However, an Extended Robust $H_{\infty}$ Filter (ERHF) can yield acceptable performance when the noises are Laplacian. In this paper, we use the Interacting Multiple Model(IMM) estimator for the problem of target tracking with glint noise. In the IMM method, two filters(ERKF and ERHF) are used in parallel to estimate the state. Computer simulations of a real target tracking shows that hybrid filter used the IMM algorithm has superior performance than a single type filter.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2002-2004
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• 2003

본 논문에서는 시스템의 모델 불확실성과 백색 가우시안이 아닌 $L_2$ 잡음이 존재하는 경우에 시스템 상태변수의 효과적인 추정을 위한 강인 필터를 제안한다. 제안된 필터는 적분이차제약조건(integral quadratic constraint)을 갖는 일반적인 비선형 불확실 시스템을 위해 선형근사화를 통하여 구성된다. 또한 제안된 필터의 중요한 특성인 변형된 $H{\infty}$ 성능 지수를 유도하고, 해석적 방법을 통해 제안된 필터의 잡음과 시스템 파라미터 불확실성에 대한 강인성을 분석하며, 시뮬레이션 결과를 통하여 제안된 필터가 추정치의 정확도를 효과적으로 향상시키는 것을 보인다.