• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.99-107
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• 2018

In this paper, we study target tracking in two dimensional space using a Extended Kalman filter(EKF), various Extended Kalman Filter and Cost-Reference Particle Filter(CRPF), which can effectively estimate the state values of nonlinear measurement equation. We introduce various Extended Kalman Filter which the Unscented Kalman Filter(UKF), the Central Difference Kalman Filter(CDKF), the Square Root Unscented Kalman Filter(SR-UKF), and the Central Difference Kalman Filter(SR-CDKF). In this study, we calculate Mean Square Error(MSE) of each filters using Monte-Carlo simulation with unknown noise statistics. Simulation results show that among the various of Extended Kalman filter, Square Root Central Difference Kalman Filter has the best results in terms of speed and performance. And, the Cost-Reference Particle Filter has an advantageous feature that it does not need to know the noise distribution differently from Extended Kalman Filter, and the simulation result shows that the excellent in term of processing speed and accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.826-832
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• 2007

A recurrent neural network has been frequently used in equalizing the channel for fast communication systems. The existing techniques, however, have mainly dealt with time-invariant chamois. The modern environments of communication systems such as mobile ones have the time-varying feature due to fading. In this paper, powerful decision feedback - recurrent neural network is used as channel equalizer for nonlinear and time-varying system, and two kinds of algorithms, such as extended Kalman filter (EKF) and sigma-point Kalman filter (SPKF), are proposed; EKF is for fast convergence and good tracing function, and SPKF for overcoming the problems which can be developed during the process of first linearization for nonlinear system EKF.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.5
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• pp.250-258
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• 2003

In this paper, an explanation regarding on-line identification of a fuzzy system is presented. The fuzzy system to be identified is assumed to be in the type of singleton consequent parts and be represented by a linear combination of fuzzy basis functions. For on-line identification, squared-cosine membership function is introduced to reduce the number of parameters to be identified and make the system consistent and differentiable. Then the parameters of the fuzzy system are identified on-line by the gradient search method and Extended Kalman Filter. Finally, a computer simulation is peformed to illustrate the validity of the suggested algorithms.

• Journal of Advanced Navigation Technology
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• v.11 no.2
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• pp.146-153
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• 2007

Autonomous unmanned vehicle is able to find the path and the way point by itself. 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.561-565
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• 2005

net-VE 환경에서 다중 사용자들이 정보를 공유하는 경우 교환되는 이벤트 트래픽을 줄이기 위하여 확장 칼만필터를 이용해 객체 이벤트의 가변적 경로예측을 한다. 다중 사용자를 지원하는 3차원 공간 정보공유는 가상환경에 대한 상태정보를 중앙 서버에서 관리하므로 일관성 유지가 용이하다는 장점이 있으나 네트워크에 과중한 부담을 주며, 메시지 병목현상, 확장성이 부족하다는 문제점이 있다. 본 논문에서는 이동되어져온 궤적의 유클리디 실즉치와 칼만 예측치와의 오차 정보인 이노베이션을 사용하여 가변적 경로예측을 하고, net-VE 공유 및 이벤트 필터링 과정을 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.449-452
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• 2024

본 논문에서는 리튬이온 배터리의 SOC(State Of Charge) 초기 정보의 정확도 향상을 위하여 확장칼만필터(EKF) 방법을 적용한 효율적 SOC 추정 알고리즘을 제안한다. 일반적인 전류적산법을 사용하는 방법은 초기 조건이 부정확한 경우에 오차가 발생하고 시간에 따라 누적 오차가 커지는 단점이 있다. 이러한 문제점 해결을 위하여 초기 SOC 추정값에 EKF 방법을 동시에 적용하는 알고리즘을 제안한다. 제안 알고리즘의 평가를 위한 실험을 통하여 제안 방법이 기존 SOC 추정 방법보다 추정 오차가 개선됨을 확인하였다.

• Bulletin of the Korean Space Science Society
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• 2006.04a
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• pp.39-39
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• 2006

• Bulletin of the Korean Space Science Society
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• 2005.04a
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• pp.43-43
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• 2005

• Journal of Astronomy and Space Sciences
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• v.22 no.4
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• pp.501-512
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• 2005

This research supposed when a fictitious KSIV-I space launch vehicle launches from NARO space center. This compared and analyzed the results from real-time trajectory estimation using the Extended Kalman Filter and the Unscented Kalman Filter. A virtual trajectory and observation data are generated for the fictitious KSLV-I and three measurement radars. The performances of both Otters are compared for several simulations with small initial errors, large initial errors, 20Hz and 10Hz data rate. The results show that the Unscented Kalman Filter yields faster convergence and more accurate than the Extended Kalman Filter for the cases with larger initial error and slower data rate conditions.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2258-2261
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• 2002

본 논문에서는 정지궤도 통신위성인 무궁화위성 3호 버스시스템을 모델로 하여 확장칼만필터를 이용한 자세결정 알고리즘을 개발하였다. 그리고 자이로에 바이어스가 있는 경우 및 자이로가 고장이 난 경우에 대한 결과를 시뮬레이션을 통해 필터의 성능을 검증하였다. 특히, 추정된 상태변수를 이용한 2Hz 자세제어를 동시에 수행하였다.