• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.4
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• pp.345-354
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• 2014

In this paper, we propose a real-time algorithm for estimating the relative position and velocity of a person with respect to a robot using a Kinect sensor and an extended Kalman filter (EKF). Additionally, we propose an algorithm for controlling the robot in the proximity of a person in a variety of modes. The algorithm detects the head and shoulder regions of the person using a histogram of oriented gradients (HOG) and a support vector machine (SVM). The EKF algorithm estimates the relative positions and velocities of the person with respect to the robot using data acquired by a Kinect sensor. We tested the various modes of proximity movement for a human in indoor situations. The accuracy of the algorithm was verified using a motion capture system.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.5
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• pp.367-377
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• 2019

As an alternative navigation system for the non-GNSS (Global Navigation Satellite System) environment, a new type of DBRN (DataBase Referenced Navigation) which applies both gravity gradient and terrain, and combines filter-based algorithm with profile matching was suggested. To improve the stability of the performance compared to the previous study, both centralized and decentralized EKF (Extended Kalman Filter) were constructed based on gravity gradient and terrain data, and one of filters was selected in a timely manner. Then, the final position of a moving vehicle was determined by combining a position from the filter with the one from a profile matching. In the simulation test, it was found that the overall performance was improved to the 19.957m by combining centralized and decentralized EKF compared to the centralized EKF that of 20.779m. Especially, the divergence of centralized EKF in two trajectories located in the plain area disappeared. In addition, the average horizontal error decreased to the 16.704m by re-determining the final position using both filter-based and profile matching solutions. Of course, not all trajectories generated improved performance but there is not a large difference in terms of their horizontal errors. Among nine trajectories, eights show smaller than 20m and only one has 21.654m error. Thus, it would be concluded that the endemic problem of performance inconsistency in the single geophysical DB or algorithm-based DBRN was resolved because the combination of geophysical data and algorithms determined the position with a consistent level of error.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2339-2345
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• 2011

This paper proposes a new multi-filtered inertial navigation system to estimate the attitude and position of moving objects. This system has two states, the one is attitude state and the other is position/velocity state. For compensating IMU sensor errors, each of the two states uses a different filter: the attitude state uses the EKF and the position state uses the UPF. The fast and precise characteristics of the EKF have been properly utilized for the attitude estimation, while superior dynamic characteristics of the UPF have been fully adopted for the position estimation. The combination of these two filters in an inertial navigation system improves the system performance to be faster and more accurate. Experimental results demonstrate the superiority of this approach comparing to the conventional ones.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1766-1769
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• 1997

Fast and accurate distillation design requires a model that significantly reduces the problem size withour loss of accruacy is especially suitable for rela-time applicatoins. the reduced order model is obtained by use of Principal Component Anlysis(PCA). Then the extended Kalman filter and the Recursie Predictiuon Error(RPE) mehtod are appliced to identify the model parameters and the feed compostion form the measuremenets of the coumn. as a consequence it is found that the model reduction thechique can account for the dynamics of the rigorous distillation model and not only the model parameters, bu also the feed compostion can be identified efficiently. this technique is applied to industrial operation data verify the performance of reduced order model.

• Bulletin of the Korean Space Science Society
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• 2011.04a
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• pp.25.4-25.4
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• 2011

Various estimation methods based on Kalman filter have been applied to the real-time satellite orbit determination. The most popular method is the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The EKF is easy to implement and to use on orbit determination problem. However, the linearization process of the EKF can cause unstable solutions if the problem has the inaccurate reference orbit, sparse or insufficient observations. In this case, the UKF can be a good alternative because it does not contain linearization process. However, because both methods are based on Gaussian assumption, performance of estimation can become worse when the distribution of state parameters and process/measurement noise are non-Gaussian. In nonlinear/non-Gaussian problems the particle filter which is based on sequential Monte Carlo methods can guarantee more exact estimation results. This study develops and tests the particle filter for satellite orbit determination. The particle filter can be more effective methods for satellite orbit determination in nonlinear/non-Gaussian environment.

• The Journal of Korea Robotics Society
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• v.5 no.4
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• pp.367-376
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• 2010

This Paper Proposes A Robust Image Stabilization System For A Mobile Robot Using An Extended Kalman Filter (Ekf). Though Image Information Is One Of The Most Efficient Data Used For Robot Navigation, It Is Subjected To Noise Which Is The Result Of Internal Vibration As Well As External Factors Such As Uneven Terrain, Stairs, Or Marshy Surfaces. The Camera Vibration Deteriorates The Image Resolution By Destroying The Image Sharpness, Which Seriously Prevents Mobile Robots From Recognizing Their Environment For Navigation. In This Paper, An Inclinometer Was Used To Measure The Vibration Angle Of The Camera System Mounted On The Robot To Obtain A Reliable Image By Compensating For The Angle Of The Camera Vibration. In Addition The Angle Prediction Obtained By Using The Ekf Enhances The Image Response Analysis For Real Time Performance. The Experimental Results Show The Effectiveness Of The Proposed System Used To Compensate For The Blurring Of The Images.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.10
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• pp.48-55
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• 2006

A multi-filter fusion technique is proposed and this technique is applied to the INS/GPS integrated system. IIR-type EKF and FIR-type RHKF filter are fused to provide the advantages of these filters based on the adaptive mixing probability calculated by the residuals and the residual covariance matrices of the filters. In the INS/GPS, this fusion filter can provide more robust navigation information than the conventional stand-alone filter.

• The Journal of Korea Robotics Society
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• v.9 no.4
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• pp.232-241
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• 2014

This paper proposes an underwater localization algorithm using probabilistic object recognition. It is organized as follows; 1) recognizing artificial objects using imaging sonar, and 2) localizing the recognized objects and the vehicle using EKF(Extended Kalman Filter) based SLAM. For this purpose, we develop artificial landmarks to be recognized even under the unstable sonar images induced by noise. Moreover, a probabilistic recognition framework is proposed. In this way, the distance and bearing of the recognized artificial landmarks are acquired to perform the localization of the underwater vehicle. Using the recognized objects, EKF-based SLAM is carried out and results in a path of the underwater vehicle and the location of landmarks. The proposed localization algorithm is verified by experiments in a basin.

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1141-1143
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• 2001

This paper presents a detailed study of the reduced-model extended Kalman filter(EKF) for estimating the rotor speed of an induction motor drive. The general structure of the Kalman filter is reviewed and the various system vectors and matrices are defined. By including the rotor speed as a state variable, the EKF equations are established from a discrete two axis model of the three-phase induction motor, using the software MATLAB/Simulink, simulation of the EKF speed estimation algorithm is carried out for an induction motor drive with indirect vector control.

• Proceedings of the KIPE Conference
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• 2014.11a
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• pp.56-57
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• 2014

본 논문에서는 $LiCoO_2$ 원통형(cylindrical)셀을 확장칼만필터(extended Kalman filter; EKF) 추정알고리즘에 적용 시 등가회로모델 차이에 따른 SOC(State-of-charge) 추정성능의 비교 분석을 진행하였다. 첫 번째, 등가회로 모델의 성능을 좌우하는 RC-ladder의 개수에 따른 SOC 추정성능의 차이를 비교하였고, 두 번째, 모델 단순화에 따른 불가피한 모델의 에러를 줄이고자 사용되는 노이즈 모델(noise model) 및 데이터 리젝션(data rejection)의 유무에 따른 SOC 추정성능을 비교분석 하였다.