• Journal of Positioning, Navigation, and Timing
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• 제8권3호
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• pp.107-117
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• 2019

When a vehicle moves with a high rotation rate, it is not easy to measure the angular velocity using an off-the-shelf gyroscope. If the angular velocity is estimated using the extended Kalman filter in the gyro-free inertial navigation system, the effect of the accelerometer error and initial angular velocity error can be reduced. In this paper, in order to improve the navigation performance of the gyro-free inertial navigation system, an angular velocity estimation method is proposed based on an extended Kalman filter with an accelerometer random bias error model. In order to show the validity of the proposed estimation method, angular velocities and navigation outputs of a vehicle with 3 rev/s rotation rate are estimated. The results are compared with estimates by other methods such as the integration and an extended Kalman filter without an accelerometer random bias error model. The proposed method gives better estimation results than other methods.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.860-863
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• 1996

In this paper, we formulate the extended Kalman filter for calibration of gimballed inertial navigation system (GINS) at a pure navigation mode with 1500 ft/sec initial velocity and compare its performance to the linear Kalman filter's by using Monte-Carlo analysis method. It has been shown that estimation performance of the extended Kalman filter is better than that of the linear Kalman filter.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.3-5
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• 2007

This paper presents decision feedback equalizers using a recurrent neural network trained algorithm using extended Kalman filter(EKF) and sigma-point Kalman filter(SPKF). EKF is propagated, analytically through the first-order linearization of the nonlinear system. This can introduce large errors in the true posterior mean and covariance of the Gaussian random variable. The SPKF addresses this problem by using a deterministic sampling approach. The features of the proposed recurrent neural equalizer And we investigate the bit error rate(BER) between EKF and SPKF.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.349-351
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• 2004

The classical dynamic backpropagation learning algorithm has the problems of learning speed and the determine of learning parameter. The Extend Kalman Filter(EKF) is used effectively for a state estimation method for a non linear dynamic system. This paper presents a learning algorithm using Dual Extended Kalman Filter(DEKF) for Fully Recurrent Neural Network(FRNN). This DEKF learning algorithm gives the minimum variance estimate of the weights and the hidden outputs. The proposed DEKF learning algorithm is applied to the system identification of a nonlinear SISO system and compared with dynamic backpropagation learning algorithm.

• 한국자동차공학회논문집
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• 제16권3호
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• pp.7-14
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• 2008

Unmanned vehicle has a performance for finding the path and the way point by itself, so called orientation and direction. For the more precise navigation performance, Extended kalman filter, which is integrated with inertial navigation system and global positioning system is proposed in this paper. Extended kalman filter's performance is evaluated by the simulation and applied to the unmanned vehicle. The test result shows the effectiveness of Extended kalman filter for the navigation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 전기.전자공학 학술대회 논문집
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• pp.905-908
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• 1988

In recent, Kalman filter technique has been much used as one of technique for tracking of the moving target. But some problem are still remained to be resolved. For example, when Kalman filter technique is applied to nonlinear system, the technique is nonoptimal estimator. Therefore, extended Kalman filter is proposed to reduce modeling error for nonlinear system. In this study, an extended Kalman filter in Cartesian coordinates is described for moving target, when the radar sensor measures range, azimuth and elevation angle in polar coordinates. And an approximate gain computation algorithm is proposed. In this approach, Kalman gains are computed for three uncoupled filter and multiplied by a Jacobian transformation determined from the measured target position and orientation.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 B
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• pp.433-436
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• 2003

The estimation of the position and the orientation for the mobile robot constitutes an important problem in mobile robot navigation. Although the odometry can be used to describe the motions of the mobile robots, there inherently exist the gaps between the real robots and the mathematical model, which may be caused by a number of error sources contaminating the encoder outputs. Hence, applying the standard extended Kalman filter for the nominal model is not supposed to give the satisfactory performance. As a solution to this problem, a new robust extended Kalman filter is proposed based on the Krein space approach. We consider the uncertain discrete time nonlinear model of the mobile robot that contains the uncertainties represented as sum quadratic constraints. The proposed robust filter has the merit of being constructed by the same recursive structure as the standard extended Kalman filter and can, therefore, be easily designed to effectively account for the uncertainties. The simulations will be given to verify the robustness against the parameter variation as veil as the reliable performance of the proposed robust filter.

• 한국항행학회논문지
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• 제20권1호
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• pp.53-58
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• 2016

우리는 본 논문에서 이차 칼만 필터를 이용한 영상 기반 객체 추적분야의 새로운 알고리즘을 제안한다. 최근에 발표된 이차 칼만 필터는 영상 기반의 객체의 실제 3차원 공간의 위치를 추적하는 것에는 아직 적용되지 않았다. 2차원 영상 내의 위치를 3차원 공간상의 위치로 환원시키는 것은 비선형적 변환을 수반하기 때문에 그에 맞는 추적 알고리즘을 사용해야만 한다. 이러한 상황에서, 비선형 수식을 이차식으로 근사화하는 이차 칼만 필터가 선형으로 근사화하는 확장 칼만 필터보다 더 정확한 성능을 낼 수 있다. 우리는 동일한 상황을 가정하여 확장 칼만 필터, 무향 칼만 필터, 파티클 필터, 그리고 우리가 제안한 이차 칼만 필터를 이용하여 객체를 추적하고, 그 결과를 비교해 본다. 결론적으로 이차 칼만 필터가 발산율이 확장 칼만 필터에 비해 거의 절반가량 감소하며, 추적 정확도 측면에서 무향 칼만 필터에 비해 1% 가량 우수한 성능을 나타낸다.

• 대한전기학회논문지
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• 제37권9호
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• pp.636-645
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• 1988

A new filter algorithm is suggested improving structurally the conventional extended Kalman filter of which the performance is dependent on the selection of the reference axes, by use of line-of-sight axes and gain rotation technique. The implementation method using microcomputer which implements tracking Kalman filter is introduced in terms of hardware and software. Then, through the simulation the performance of suggested filter is compared with that of conventional extended Kalman filter and the possibility of the real time tracking of moving target is investigated.

• 한국통신학회논문지
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• 제37A권11호
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• pp.1001-1011
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• 2012

본 논문에서는 IEEE 802.11 시스템에서 경쟁 중인 터미널 수를 추정하고 이를 반영할 때 시스템 성능에 미치는 영향을 분석한다. IEEE 802.11 시스템에서는 터미널간의 다중 접근의 방법으로 DCF (Distributed Coordination Function)를 이용하고 있으며 경쟁하는 터미널 수를 정확하게 추정하여 반영하는 것이 시스템 throughput 증가하는데 중요한 요소가 된다. 본 논문에서는 터미널 수를 추정하는 방법으로 노이즈 정보가 필요하지 않는 Extended H Infinity Filter (EHIF)를 이용하여 터미널 수를 추정하는 방법을 제안한다. 경쟁하는 터미널의 수가 saturated되는 경우와 non-saturated되는 네트워크 환경에서 EHIF가 기존의 Extended Kalman Filter (EKF) 방법보다 좋은 성능을 가짐을 모의실험을 통해 확인하였고 이를 정량적으로 분석하였다.