• 전자공학회논문지SC
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• 제46권3호
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• pp.22-30
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• 2009

GPS 와 INS 통합시스템은 저가 MEMS 기술의 결과에 따라 대중적으로 널리 사용되기에 이르렀다. 그러나 저가센서에 의한 현재의 성과는 관성센서의 큰 에러 때문에 여전히 낮은 실정이다. 이것은 제한된 도시환경 안에서의 비행범위 때문에 더욱 관련이 있다. 이러한 관성센서 에러를 줄이면서 동시에 위성의 활용성을 높이기 위하여 GPS 와 저가 INS 는 연성으로 결합되어 Kalman Filter 설계를 응용하여 상호 통합되어진다. 본 논문에서는 연성으로 결합된 Kalman Filter를 이용한 GPS/INS 센서 통합을 제공한다. 우리는 또한 경로의 기하학에 의해 또는 그 목적시간 위치 따라 수학적으로 설명하는 ZH45C 궤도장치에 의한 산출된 기준 Wander Azimuth Strapdown Mechanization의 시뮬레이터 결과를 비교하여 검증하다.

• 로봇학회논문지
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• 제4권2호
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• pp.112-120
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• 2009

In this paper, a localization error recoverymethod based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2433-2435
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• 2000

Inertial Navigation System(INS) provides short-term accurate navigation solution but its error grows with time due to integration characteristics. Meanwhile, Global Positioning System(GPS) provides long-term stable solution but it has poor error characteristics in high dynamic region. So for its synergistic relationship, an integrated INS/GPS systems has been widely used as an advanced navigation system. Generally, two kinds of integration method are used. One is loosely coupled mode which uses GPS-derived position and velocity as measurements in an integrated Kalman filter. The other is tightly coupled one which uses pseudorange and pseudorange rate as Kalman filter measurements. In this paper the system error models and observation models for two kinds of integrated systems are derived, respectively, and their performance are compared through Monte-Carlo simulations.

• 제어로봇시스템학회논문지
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• 제22권11호
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• pp.937-943
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• 2016

Vertical velocity is critical in many areas, such as the control of unmanned aerial vehicles, fall detection, and virtual reality. Conventionally, the integration of GPS (Global Positioning System) with an IMU (Inertial Measurement Unit) was popular for the estimation of vertical components. However, GPS cannot work well indoors and, more importantly, has low accuracy in the vertical direction. In order to overcome these issues, IMU-barometer integration has been suggested instead of IMU-GPS integration. This paper proposes a new complementary filter for the estimation of vertical velocity based on IMU-barometer integration. The proposed complementary filter is designed to minimize drift error in the estimated velocity by adding PID control in addition to a zero velocity update technique.

• 한국군사과학기술학회지
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• 제9권1호
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• pp.5-12
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• 2006

This study describes an SDINS/GPS/ZUPT integration algorithm for land navigation systems. The SDINS error can be decoupled in two parts. The first part is the the Schuler component which does not depend on object motion parameters, and the other is the Non-Schuler part which depends on the product of object acceleration and azimuth error. Azimuth error causes SDINS error in proportion to the traversed distance. The proposed system consists of a GPS/SDINS integration system and an SDINS/ZUPT integration system, which are both realized by an indirect feedforward Kalman filter. The main difference between the two is whether the estimate includes the Non-Schuler error or not, which is decided by the measurement type. Consequently, subtracting GPS/SDINS outputs from SDINS/ZUPT outputs provide the Non-Schuler error information which can be applied to improving azimuth accuracy. Simulation results using the raw data obtained from a van test attest that the proposed SDINS/GPS/ZUPT system is capable of providing azimuth improvement.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2003년도 춘계학술대회 논문집
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• pp.1055-1058
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is the use of different model (CAD model etc.) known a priori. Also fusion or multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Voting-based fusion of cues is adapted. In voting. a very simple or no model is used for fusion. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters. namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• 전자공학회논문지
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• 제50권6호
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• pp.267-275
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• 2013

GPS가 가지는 특징과 비선형, 비가우시안의 시스템에서도 강인한 특성을 지닌 파티클 필터(PF, Particle Filter)를 이용하여 위치 추정 성능을 향상시키는 방법에 대해 제안한다. 그리고 제안한 알고리즘으로 보정한 GPS 데이터와 관성센서를 저가형 시스템에 적합한 약결합 방식을 이용하여 결합하였으며 정확도 향상을 위해 자세에 관한 칼만필터를 추가시켜 구현하였다. 구현된 시스템의 성능확인을 위해 NovAtel사의 고정밀 GPS와 비교 분석하였다.

• 제어로봇시스템학회논문지
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• 제7권3호
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• pp.269-275
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• 2001

In land navigation applications, two kinds of GPS/DR integration schemes are commonly used; the loosely-coupled integration scheme and the tightly-coupled one. The loosely-coupled integration filter has a simple structure and is easy to implement. When the number of visible satellites is insufficient, however, it cannot calibrate the errors of the DR sensors. On the contrary the tigthly-coupled integration filter can sup-press the growth of the error in the DR output even when the visibility is poor. However, it has larger com-putation load due to the state dimension and is inconsistent because of the variation in the measurement dimension. This paper presents a GPS/DR integration scheme with dual integration mode. During when the number of visible satellites is sufficient, the proposed scheme operates in a loosely-coupled integration mode. When the visibility becomes poor, it is switched into a tightly-coupled integration mode. Consequently, the pro-posed scheme can calibrate the DR sensors even when the visibility is poor. In addition, its computation time remains constant even if the number of visible satellites increases. Field experiment results show that the performance of the proposed integration method is almost similar to that of the tightly-coupled one.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.798-802
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper, the robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is to use different models (CAD model etc.) known a priori. Also fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Because voting is a very simple or no model is needed for fusion, voting-based fusion of cues is applied. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters, namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.259-264
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• 2006

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. The conventional way of determining Q and R requires good a priori knowledge of the process noises and measurement errors, which normally comes from intensive empirical analysis. Many adaptive methods have been developed to overcome the conventional Kalman filter's limitations. Starting from covariance matching principles, an innovative adaptive process noise scaling algorithm has been proposed in this paper. Without artificial or empirical parameters to be set, the proposed adaptive mechanism drives the filter autonomously to the optimal mode. The proposed algorithm has been tested using road test data, showing significant improvements to filtering performance.