• Proceedings of the Korean Society of Computer Information Conference
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• 2011.01a
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• pp.265-268
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• 2011

본 논문은 시각 장애인 및 보도 보행에 어려움을 갖는 사람들에게 안전한 보도 보행을 돕기 위한 보도 및 차도 영역 추출을 위해 보도 및 차도의 라인검출 기법을 제안한다. 사람의 눈높이에서 영상을 취득, 자연영상에서 입력된 잡음 및 노이즈를 제거하고, 캐니 에지 맵 추출, 허프 변환을 통해 보도/차도의 라인을 추출한다. 추출된 라인은 본 논문에서 제안한 방법으로 유효라인을 얻게 되며, 얻어진 유효 라인의 교점을 통해 소실점 영역을 생성, 이후 추출되는 라인의 기준이 된다. 제안된 방법은 자연영상의 보도 위치에서 보도와 차도의 올바른 라인을 추출하는데 강인함을 실험을 통해 검증하였다.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.309-313
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• 1998

고성능 서보 전동기 시스템을 구축하기 위해서는 저속영역과 고속영역을 포함하는 넓은 속도영역에서의 정확한 속도검출을 통한 정밀한 속도제어기 필수적이며, 관성모멘트와 같은 전동기의 파라메터 변동에 대해 강인한 속도제어와 외란 억제능력도 중요한 요소로서 고려되어야 한다. 변동하는 부하의 관성모멘트을 식별하여 PI 속도제어기를 실시간으로 적응 동정하고, 플랜트 잡음과 측정잡음을 고려하는 상태 관측기인 칼만필터의 부하관성에 대한 민감성을 제거하기 위해 이를 적응 동정하여 적응 상태 추정기를 구현함으로써 우수한 속도 추정 성능을 얻었다. 또한 외란과 불확실한 모델링은 등가 외란으로 추정되어 전향적으로 보상된다. 본 논문에서는 이러한 특징을 이용하여 전동기의 고성능 속도제어를 구현하고 유도전동기를 이용한 실험을 통하여 연구결과의 유효성을 확인한다.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.620-623
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• 1997

This paper is concerned with the design of a suboptimal Kalman filter with robust state estimation performance for system models represented in the state space, which are subjected to parameter uncertainties in both the state and measurement matrices. Under the assumption that the uncertain system is quadratically stable, if the augmented system composed of the uncertain system and the filter is controllable, the proposed filter can provide the upper bound of the estimation error variance for all admissible uncertain parameters. This upper bound can be represented as the convex function of a parameter introduced in the design procedure, and the optimized upper bound of the estimation error variance can also be found via the optimization of this convex function.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.22-30
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• 2003

A Coordinate-Transformation Extended Robust Kalman Filter (CERKF) designed in the Krein space is proposed, and then applied to a nonlinear incoming ballistic missile tracking system with parameter uncertainties. First, the Extended Robust Kalman filter (ERKF) is proposed to handle the nonlinearity of measurement equation which occurs whenever the polar coordinate system is transformed into the Cartesian coordinate system. Moreover, linearization error inevitably occurs and deteriorates the tracking performance, which is considerably reduced by the proposed CERKF. Through the simulation results, we show that the proposed CERKF, which uses the measurement coordinate system, has less RMS error than the previous ERKF which is designed in the Krein space using the Cartesian system. We also verify that the robustness and the stability of the proposed filter are guaranteed in two radars: the phased way radar and the scanning radar

• The Transactions of the Korean Institute of Electrical Engineers
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• v.40 no.12
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• pp.1302-1307
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• 1991

This paper presents robustness properties of Kalman filters for linear time-invariant systems with delays in both the state and the output. The circle condition concerning the return difference matrix is derived. From the circle condition, it can be seen that the Kalman filter guarantees such nondivergence margins as (1/2,$\infty$) gain margin and $\pm$60$^{\circ}$phase margin, which are the same as those for ordinary systems. The results in this paper might be expected to make theoretical background on extending the LQG/LTR method to systems with delay in the output.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.267-275
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• 2013

EKF has been widely used for GPS/INS integration as standard method but EKF has one well-known drawback. if the errors are not within the bounded region, the filter may be divergent. The particle filter has the advantage of the nonlinear and non-gaussian system. This paper proposes a method for compensating the GPS position errors based on the particle filter and presents loosely-coupled GPS/INS integration using proposed algorithm. We used GPS position pattern with particle filter and added attitude kalman filter for improving attitude accuracy. To verify the performance, the proposed method is compared with high cost GPS as reference. In the experimental result, we verified that the accuracy and robust were well improved by the proposed method filter effectively and robustness than by original loosely-coupled integration when vehicle turns at corner.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.758-765
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• 2001

This paper proposes a method to automatically extract stable visual landmarks from sensory data. Given a 2D occupancy map, a mobile robot first extracts vertical line features which are distinct and on vertical planar surfaces, because they are expected to be observed reliably from various viewpoints. Since the feature information such as position and length includes uncertainty due to errors of vision and motion, the robot then reduces the uncertainty by matching the planar surface containing the features to the map. As a result, the robot obtains modeled stable visual landmarks from extracted features. This extraction process is performed on-line to adapt to an actual changes of lighting and scene depending on the robot’s view. Experimental results in various real scenes show the validity of the proposed method.

• Proceedings of the KIEE Conference
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• 2000.11d
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.2
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• pp.93-100
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• 2006

In this paper, a new-type Extended Kalman Filter (EKF) is proposed as a robust nonlinear filter for a stochastic nonlinear system. The original EKF is widely used for various nonlinear system applications. But it is fragile to its estimation errors because they give rise to linearization errors that affect the system mode1 as the modeling errors. The linearization errors are nonlinear functions of the estimation errors therefore it is very difficult to obtain the accurate error covariance of the EKF using the linear form. The inaccurately estimated error covariance hinders the EKF from being a sub-optimal estimator. The proposed filter tries to obtain the upper bound of the error covariance tolerating the uncertainty of the error covariance instead of trying to obtain the accurate one. It treats the linearization errors as uncertain modeling errors that can be handled by the robust linear filtering. In order to be more robust to the estimation errors than the original EKF, the proposed filter minimizes the upper bound like the robust linear filter that is applied to the linear model with uncertainty. The in-flight alignment problem of the inertial navigation system with GPS position measurements is a good example that the proposed robust filter is applicable to. The simulation results show the efficiency of the proposed filter in the robustness to initial estimation errors of the filter.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.4
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• pp.308-315
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• 2016

A new technique for Attitude & Heading Reference System (AHRS) by using sequential measurement noise covariance (SMNC) is addressed in a vibration environments in this paper. In particular, a low-cost inertial measurement unit in general diverges in the acceleration phase or vibrating environments due to inherent properties of gravity and acceleration. In this paper, by considering current and prior measurements to estimate actual attitudes and headings in a local frame, the proposed technique overcomes these problems efficiently. Finally, the performance of the suggested approach is verified by numerical simulations.