• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1675-1678
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• 1997

In this paper, an attitude determination system(ADS) for KOMPSAT using GPS LI carrier phase measurements is considered. The baseline vector is estimated by the Exetnded Kalman Filter (EKF) which used the double differenced carrier phased measuremenmts made by three GPS receivers mounted on the spaceraft. The attitude angles of three axes of spacecrat are computed by the estimated baseline vectors, directly. The proposed ADS is verified by the simulation results.

• Journal of Astronomy and Space Sciences
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• 제26권1호
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• pp.59-74
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• 2009

Relative navigation system is presented using GPS measurements from a single-channel global positioning system (GPS) simulator. The objective of this study is to provide the real-time inter-satellite relative positions as well as absolute positions for two formation flying satellites in low earth orbit. To improve the navigation performance, the absolute states are estimated using ion-free GRAPHIC (group and phase ionospheric correction) pseudo-ranges and the relative states are determined using double differential carrier-phase data and singled-differential C/A code data based on the extended Kalman filter and the unscented Kalman filter. Furthermore, pseudo-relative dynamic model and modified relative measurement model are developed. This modified EKF method prevents non-linearity of the measurement model from degrading precision by applying linearization about absolute navigation solutions not about the priori estimates. The LAMBDA method also has been used to improve the relative navigation performance by fixing ambiguities to integers for precise relative navigation. The software-based simulation has been performed and the steady state accuracies of 1 m and 6 mm ($1{\sigma}$ of 3-dimensional difference errors) are achieved for the absolute and relative navigation using EKF for a short baseline leader/follower formation. In addition, the navigation performances are compared for the EKF and the UKF for 10 hours simulation, and relative position errors are mm-level for the two filters showing the similar trends.

• 한국통신학회논문지
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• 제37권5B호
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• pp.370-376
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• 2012

CSS(Chirp Spread Spectrum)는 WPAN(Wireless Personal Area Network) 환경에서 SDS-TWR(Symmetric Double Sided - Two Way Ranging) 기반의 위치인식 시스템을 구현하는 기술로 사용된다. 그러나 CSS의 SDS-TWR은 전파 및 장애물과 같은 환경에 따른 간섭으로 인해 레인징 오차가 발생한다. 따라서 위치인식 시스템 개발을 위해서는 이를 보정하기 위한 보정 알고리즘이 요구된다. 본 논문은 위치인식 정확도 성능 향상을 위하여 AEDR(Algorithm of Equivalent Distance Rate) 알고리즘과 칼만필터가 적용된 KF_EDR(Kalman Filter and Equivalent Distance Rate) 보정 알고리즘을 제안하고, 그 성능을 분석 및 평가하였다. 실험 결과, KF_EDR은 AEDR 알고리즘에 비해 위치인식 정확도를 복도 그리고 운동장에서 각각 10.5%, 4.2% 더 개선시켰다. 이 결과는 위치인식 데이터의 신뢰성을 향상시킴 으로써 실제 위치인식 시스템 구현에 상당한 도움을 줄 수 있을 것으로 판단된다.

• 한국항행학회논문지
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• 제2권1호
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• pp.3-10
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• 1998

GPS는 기존의 주 용용분야인 항법시스템으로서 뿐만 아니라 최근 들어와서는 보정위성항법의 원리와 미지정수(Integer Cycle Ambiguity)결정 알고리즘을 이용하여 GPS수신기와 3~4개의 안테나로 자세결정을 하는데 이용되고 있다. 본 연구에서는 고가의 패치(Patch) 안테나를 장착한 수신기를 사용하지 않고 저가의 수신기 여러 대를 이용하여 자세결정을 할 수 있도록 이중차분(Double Difference)을 한 측정식을 이용하여 칼만필터를 통해 자세결정을 할 수 있는 알고리즘을 제시하고 모의실험을 통해 제시한 알고리즘의 유용성을 고찰한다.

• 반도체디스플레이기술학회지
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• 제22권2호
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• pp.112-119
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• 2023

In urban environments, signals of Global Positioning System (GPS) can be blocked and reflected by tall buildings, large vehicles, and complex components of road network. Therefore, the performance of the positioning system using the GPS module in urban areas can be degraded due to the loss of GPS signals necessary for the position estimation. To deal with this issue, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope and accelerometer, and Bayesian filters, such as Kalman filter (KF) and particle filter (PF), have been designed to enhance the performance of the GPS-based positioning system. Among Bayesian filters, the PF has been widely used for the target tracking and vehicle navigation, since it can provide superior performance in estimating the state of a dynamic system under nonlinear/non-Gaussian circumstance. This paper presents a positioning system that uses the double-stacked particle filter (DSPF) as well as the accelerometer, gyroscope, and GPS receiver on the smartphone to provide higher pedestrian positioning accuracy in urban environments. The DSPF employs a nonparametric technique (Parzen-window) to create the multimodal target distribution that approximates the posterior distribution. Experimental results show that the DSPF-based positioning system can provide the significant improvement of the pedestrian position estimation in urban environments.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.192-194
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• 1995

An improved filter for tracking a maneuvering target is presented. The proposed filter consists of two kalman filters based on different dynamic models and double decision logic. The use of double decision logic for the maneuver onset and ending detection leads to reduction in estimation error. This decision rule is based on evidence theory, Dempster-Shafer theory, which is extended in order to be applicable in the tracking problem. Simulation results show that the proposed filter performs better than IMM at a lower computational load.

• 한국정보과학회논문지:정보통신
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• 제34권3호
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• pp.156-167
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• 2007

본 논문에서는 VBR(Variable-Bit-Rate) 트래픽의 비선형적이고 버스티한 특성을 모델화 한 GOP ARIMA(ARIMA for Group Of Pictures) 모델을 칼만 필터 알고리즘을 이용하여 실시간으로 예측하는 기법을 제안한다. 칼만 필터를 이용한 예측 기법은 GOP ARIMA의 상태공간 모델링 과정과 향후 N초 간의 트래픽을 예측하는 과정으로 구성된다. 실험을 위해 GOP의 크기가 각각 15인 세 가지 종류의 MPEG VBR 트래픽(뉴스, 드라마, 스포츠)을 제작하였고, 칼만 필터를 이용한 세 가지 종류의 트래픽의 예측 결과를 선형 예측법과 이중 지수 평활법을 이용해 예측한 결과와 비교해 예측 성능이 상대적으로 우수함을 확인할 수 있었다. 또한 예측값에 신뢰 구간을 설정하는 신뢰 구간 분석법을 통해 트래픽 관점에서 장면 변화를 예측하는 방법을 제시하였다. 본 논문의 칼만 필터 기반의 예측 알고리즘은 MPEG 기반 VBR 트래픽을 비롯한 기타 인터넷 트래픽을 실시간으로 예측하는 방법과 이를 이용해 인터넷 서버의 설계 및 자원 할당 정책 등을 위한 트래픽 엔지니어링 연구에 기여할 수 있을 것이다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.345-347
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• 2006

An interferometer is the unique measurement device that can measure the range up to a few meters with sub-nano accuracy and this characteristic makes it as the important sensing device for the emerging nano-mechatronics technologies. The interferometer, however, is very sensitive to the environments such as temperature, humidity, sound noises, vibrations and air turbulences and these factors result in a few hundred nano meter errors. There have been many efforts to reduce these environmental errors. These efforts are mainly focused in reducing the errors inside the interferometer and improving the environments physically. The purpose of this paper is to improve accuracy of the interferometer by using measurement noise models and the Kalman filter algorithm.

• Ocean and Polar Research
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• 제30권2호
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.1162-1179
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• 2017

In this paper, we propose indoor location tracking method using RSSI(Received Signal Strength Indicator) value received from BLE(Bluetooth Low Energy) beacon. Due to the influence of various external environmental factors, it is very difficult to improve the accuracy in indoor location tracking. In order to solve this problem, we propose a novel method of reducing the noise generated in the external environment by using a double Gaussian filter. In addition, the value of the RSSI signal generated in the BLE beacon is different for each device. In this study, we propose a method to allocate additional weights in order to compensate the intensity of signal generated in each device. This makes it possible to improve the accuracy of indoor location tracking using beacons. The experiment results show that the proposed method effectively decrease the RSSI deviation and increase location accuracy. In order to verify the usefulness of this study, we compared the Kalman filter algorithm which is widely used in signal processing. We further performed additional experiments for application area for indoor location service and find that the proposed scheme is useful for BLE-based indoor location service.