• 한국정보과학회논문지:소프트웨어및응용
• /
• 제31권2호
• /
• pp.245-254
• /
• 2004

이 논문은 카메라가 연속적으로 움직일 때 그 카메라로부터 얻은 동영상을 분석하여 카메라의 움직임에 대한 정보와 영상내의 구조물의 삼차원 정보를 계산하는 알고리듬에 대한 것이다. 일반적으로 불 연속한 위치에서 얻은 영상의 집합으로부터 삼차원정보 및 카메라 정보를 얻는 경우에는 카메라의 움직임에 대한 제약조건이 필요 없지만, 비디오 카메라를 이용하여 동영상을 취득하는 경우에는 항상 카메라의 움직임이 부드러워야 한다는 조건이 따라 붙는다. 따라서, 이 논문에서는 ‘부드러운 움직임을 가지는 카메라’라는 제약조건을 포함하는 카메라 및 삼차원정보의 최적화 과정에 대하여 연구하였다. 목적하는 바를 얻기 위하여 Expectation-Maximization 방법을 사용하여 카메라의 움직임에 대한 모델 파라메터를 동시에 추정하였는데, 이를 위하여 Extended Kalman Filter 와 Extended Kalman Smoother를 적용하였다. 이 연구는 길이가 긴 비디오 영상열의 비젼 해석에 기본이 된다. 실제 영상을 이용하여 실험한 결과를 보였다.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1996년도 춘계학술대회 논문집
• /
• pp.254-258
• /
• 1996

In this paper, we proposed realtime visual tracking system of moving object for 2D target using extended Kalman Filter Algorithm. A targeting marker are recongnized in each image frame and positions of targer object in each frame from a CCD camera while te targeting marker is attached to the tip of the SCARA robot hand. After the detection of a target coming into any position of the field-of-view, the target is tracked and always made to be located at the center of target window. Then, we can track the moving object which moved in inter-frames. The experimental results show the effectiveness of the Kalman filter algorithm for realtime tracking and estimated state value of filter, predicting the position of moving object to minimize an image processing area, and by reducing the effect by quantization noise of image

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

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 2004년도 ICCAS
• /
• pp.1669-1674
• /
• 2004

In this paper, a nonlinear information filter (IF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant-speed turn model for curvilinear motions. For the constant-speed turn model, a nonlinear IF is used in place of the extended Kalman filter in nonlinear systems. The suggested algorithm reduces the root mean squares error for linear motions and rapidly detects possible turning motions.

• International Journal of Aeronautical and Space Sciences
• /
• 제15권3호
• /
• pp.258-266
• /
• 2014

We present a system for the real-time visual relative navigation of a fixed-wing unmanned aerial vehicle in a GPS-denied environment. An extended Kalman filter is used to construct a vision-aided navigation system by fusing the image processing results with barometer and inertial sensor measurements. Using a mean-shift object tracking algorithm, an onboard vision system provides pixel measurements to the navigation filter. The filter is slightly modified to deal with delayed measurements from the vision system. The image processing algorithm and the navigation filter are verified by flight tests. The results show that the proposed aerial system is able to maintain circling around a target without using GPS data.

• Smart Structures and Systems
• /
• 제15권2호
• /
• pp.409-424
• /
• 2015

Structural nonlinearity is a common phenomenon encountered in engineering structures under severe dynamic loading. It is necessary to localize and identify structural nonlinearities using structural dynamic measurements for damage detection and performance evaluation of structures. However, identification of nonlinear structural systems is a difficult task, especially when proper mathematical models for structural nonlinear behaviors are not available. In prior studies on nonparametric identification of nonlinear structures, the locations of structural nonlinearities are usually assumed known and all structural responses are measured. In this paper, an identification algorithm is proposed for locating and identifying model-free structural nonlinearities and systems using incomplete measurements of structural responses. First, equivalent linear structural systems are established and identified by the extended Kalman filter (EKF). The locations of structural nonlinearities are identified. Then, the model-free structural nonlinear restoring forces are approximated by power series polynomial models. The unscented Kalman filter (UKF) is utilized to identify structural nonlinear restoring forces and structural systems. Both numerical simulation examples and experimental test of a multi-story shear building with a MR damper are used to validate the proposed algorithm.

• 전력전자학회:학술대회논문집
• /
• 전력전자학회 1999년도 전력전자학술대회 논문집
• /
• pp.671-674
• /
• 1999

In the PWM converter, PLL(Phase Locked Loop) is usually used as a tool which senses the angle of input voltage. This is sensitive to nois and needs additional hardware. In this work, we propose a sensorless control scheme of PWM converter using EKF(Extended Kalman Filter). EKF estimates a phase angle of input voltage from nonlinear state equation using measured phase currents. We control power factor and DC-link voltage utilizing the estimated phase angle. We demonstrate the effectiveness of the proposed estimation algorithm through simulations.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
• /
• pp.611-615
• /
• 1991

A technique for estimation of the hydrodynamic parameter of an underwater vehicle is presented. An extended, augmented Kalman Filter is used to extract the hydrodynamic parameter. Computer generated data were used for the measurement information in lieu of actual run data. The feasibility of identifying values of the hydrodynamic parameter of an underwater vehicle is studied. Computer simulation are done in order to validate the performance of the proposed algorithm.

• 음성과학
• /
• 제11권4호
• /
• pp.53-66
• /
• 2004

The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech. we assume that speech is the output of a nonlinear prediction HMM (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parameters are controlled by a hidden Markov chain. The simulation results shows that the proposed method offers better performance gains relative to the previous results [6] with slightly increased complexity.

• Smart Structures and Systems
• /
• 제15권1호
• /
• pp.57-80
• /
• 2015

Extended Kalman Filter (EKF) has been widely used for structural identification and damage detection. However, conventional EKF approaches require that external excitations are measured. Also, in the conventional EKF, unknown structural parameters are included as an augmented vector in forming the extended state vector. Hence the sizes of extended state vector and state equation are quite large, which suffers from not only large computational effort but also convergence problem for the identification of a large number of unknown parameters. Moreover, such approaches are not suitable for intelligent structural damage detection due to the limited computational power and storage capacities of smart sensors. In this paper, a two-stage and two-step algorithm is proposed for the identification of structural damage as well as unknown external excitations. In stage-one, structural state vector and unknown structural parameters are recursively estimated in a two-step Kalman estimator approach. Then, the unknown external excitations are estimated sequentially by least-squares estimation in stage-two. Therefore, the number of unknown variables to be estimated in each step is reduced and the identification of structural system and unknown excitation are conducted sequentially, which simplify the identification problem and reduces computational efforts significantly. Both numerical simulation examples and lab experimental tests are used to validate the proposed algorithm for the identification of structural damage as well as unknown excitations for structural health monitoring.