• 한국군사과학기술학회지
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• 제12권1호
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• pp.114-122
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• 2009

An adaptive Kalman filter is designed as a post-navigation filter to improve the accuracy of GPS receiver's navigation performance in high dynamic environments. Not only the adaptive Kalman filter reduces the large noise error of navigation data which is obtained by least square method, but also the filter is not degraded as normal Kalman filter in high acceleration movements because the system noise is estimated. Also an initialization structure of the filter is desisted in consideration for irregular output condition of navigation data by least squared method such as reacquisition status in GPS receiver. The filter performance is verified by GPS simulator which has the simulation capability of high velocity and acceleration. Finally, a vehicle test including DGPS is executed to conform the real improvement of that filter performance. This filter can be applied to various data measurement systems to improve accuracy in high dynamic conditions besides GPS receiver.

• 제어로봇시스템학회논문지
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• 제21권11호
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• pp.1057-1063
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• 2015

This paper proposes a new nonlinear filtering algorithm that combines the unscented Kalman filter (UKF) and the finite impulse response (FIR) filter. The proposed filter is called the unscented Kalman/FIR hybrid filter (UKFHF). In the UKFHF algorithm, the UKF is used as the main filter, which produces state estimates under ideal conditions. When failures of the UKF are detected, the FIR filter is operated. Using the output of the FIR filter, the UKF is reset and rebooted. In this way, the UKFHF recovers from failures. The proposed UKFHF is applied to indoor human localization using wireless sensor networks. Through simulations, the performance of the UKFHF is demonstrated in comparison with that of the UKF.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.1771-1776
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• 2003

In mobile robot navigation, one of the key problems is the pose estimation of the mobile robot. Although the odometry can be used to describe the motions of the mobile robots quite simple and accurately, the validities of the models are limited by a number of error sources contaminating the encoder outputs so that applying the conventional extended Kalman filter to these nominal model does not yield the satisfactory performance. As a remedy for this problem, we consider the uncertain nonlinear kinematic model of the mobile robot that contains the norm bounded uncertainties and also propose a new robust extended Kalman filter based on the Krein space approach. The proposed robust filter has the same recursive structure as the conventional extended Kalman filter and can hence be readily designed to effectively account for the uncertainties. The computer simulations will be given to verify the robustness against the parameter variation as well as the reliable performance of the proposed robust filter.

• 한국항해학회지
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• 제6권1호
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• pp.61-72
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• 1982

As it is well-known, in recent years the Kalman filter has been extensively used in the engineering field. The authors tried to apply the extended Kalman filter for optimal estimation of ship's positiion which is fixed by simultaneous visual (or radio) bearings to two known locations. In practical piloting, bearings are generally measured intermittently, so in this case the original Kalman filter can not be applied because of the long sampling time. In this paper, the extended Kalman filter is revised in order to be used in such an unfavorable case, adn the Digital simulation is conducted by using the revised extended Kalman filter under various noise conditiions. Good results have been obtaiend and effectiveness of the proposed filter has been confirmed.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.104-109
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• 2002

A new robust Kalman filter is designed for the linear discrete-time system with norm-bounded parametric uncertainties. Sum quadratic constraint, which describes the uncertainties of the system, is converted into an indefinite quadratic form to be minimized in indefinite inner product space. This minimization problem is solved by the new robust Kalman filter. Since the new filter is obtained by simply modifying the conventional Kalman filter, robust filtering scheme can be more readily designed using the proposed method in comparison with the existing robust Kalman filters. A numerical example demonstrates the robustness and the improvement of the proposed filter compared with the existing filters.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.188-188
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• 2000

The most important problem in target 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 baged 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. A nonlinear dynamic equation with parameter uncertainties is used to express the uncertain system model mathematically, and a measurement equation is represented by a nonlinear equation to show data from the radar in a Cartesian coordinate frame. To solve the robust nonlinear filtering 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 tracking example.

• 한국지능시스템학회논문지
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• 제15권5호
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• pp.552-559
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• 2005

실시간 순환형 훈련 알고리즘(RTRL)과 같이 경사법에 의해 훈련되는 순환형 뉴럴 네트웍(RNN)은 수렴속도가 매우 느린 단점을 지니고 있다. 이 알고리즘은 또한 오차 역전달 처리과정에서 결코 쉽지 않은 미분 계산을 필요로 한다. 본 논문에서는 완전하게 결합된 RNN의 훈련을 위하여 소위 언센티드 칼만필터라고 불리우는 미분없는 칼만필터 훈련 알고리즘을 시스템의 상태공간 상에서 표현하였다. 미분없는 칼만필터 훈련 알고리즘은 순환형 뉴럴 네트웍 훈련시 미분 계산 없이 매우 빠른 수렴속도와 좋은 추정 성능을 보여준다. 비선형 채널 등화 실험을 통하여 미분 없는 칼만필터 훈련 알고리즘을 이용한 RNN의 성능이 향상되었음을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.351-353
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• 2006

In this paper, we present the traditional GPS Position- Velocity (PV) model to apply for both Discrete-Time Kalman Filter and Discrete-Time $H_{\infty}$ Filter. The positioning algorithms of both filters are proposed for a stand-alone low-cost GPS module to increase its accuracy. For disturbance cancellation, the Kalman Filter requires the statistical information about process and measurement noises while the $H_{\infty}$ Filter only requires that these noises are bounded. Experiments show that with the same measurement data, $H_{\infty}$ Filter gives us better positioning results compared with Least-Squared method and Kalman Filter.

• 한국항공우주학회지
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• 제34권8호
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• pp.71-78
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• 2006

본 논문에서는 직교 좌표계에서 추적필터가 설계될 때, 표적의 거리와 방위에 대한 관측오차 공분산의 변화를 고려하기 위하여 정상상태 칼만필터의 해석적 해를 이용하는 IMM 추적기를 설계하였다. 제안된 정상상태 칼만필터 기반 IMM 추적기의 성능분석 및 검증을 위하여 거리의 변화가 작은 표적과 거리의 변화가 큰 표적에 대하여 각각 100회의 Monte Carlo 시뮬레이션을 수행하고, 고정이득 및 칼만필터 기반의 IMM 추적기와 RMS 오차분석을 통하여 비교하였다. 모의실험 결과로부터 제안된 방법이 칼만필터 기반 IMM 추적필터에 비하여 연산량을 크게 감소시킬 수 있으며, 유사한 추적성능을 제공할 수 있음을 확인하였다.

• 대한조선학회논문집
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• 제57권3호
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• pp.175-181
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• 2020

For the success of a structural integrity management, it is essential to acquire structural response data at some critical locations with limited number of sensors. In this study, the structural response of numerical model was estimated by data fusion approach based on the Kalman filter known as stochastic recursive filter. Firstly, transient direct analysis was conducted to calculate the acceleration and strain of the numerical standing beam model, then the noise signals were mixed to generate the numerical measurement signals. The acceleration measurement signal was provided to the Kalman filter as an information on the external load, and the displacement measurement, which was transformed from the strain measurement by using strain-displacement conversion relationship, was provided into the Kalman filter as an observation information. Finally, the Kalman filter estimated the displacement by combining both displacements calculated from each numerically measured signal, then the estimated results were compared with the results of the transient direct analysis.