• Journal of Positioning, Navigation, and Timing
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• 제6권3호
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• pp.105-116
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• 2017

Several wireless localization algorithms are evaluated for the IR-UWB-based indoor location with the assumption that the ranging measurements contain the channelwise Non-Calibration Error (NCE). The localization algorithms can be divided into the Model-free Localization (MfL) methods and Model-based Kalman Filtering (MbKF). The algorithms covered in this paper include Iterative Least Squares (ILS), Direct Solution (DS), Difference of Squared Ranging Measurements (DSRM), and ILS-Common (ILS-C) methods for the MfL methods, and Extended Kalman Filter (EKF), EKF-Each Channel (EKF-EC), EKF-C, Cubature Kalman Filter (CKF), and CKF-C for the MbKF. Experimental results show that the DSRM method has better accuracy than the other MfL methods. Also, it demands smallest computation time. On the other hand, the EKF-C and CKF-C require some more computation time than the DSRM method. The accuracy of the EKF-C and CKF-C is, however, best among the 9 methods. When comparing the EKF-C and CKF-C, the CKF-C can be easily used. Finally, it is concluded that the CKF-C can be widely used because of its ease of use as well as it accuracy.

• 로봇학회논문지
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• 제3권1호
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• pp.16-22
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• 2008

Recently, automatic parking assist systems are commercially available in some cars. In order to improve the reliability and the accuracy of parking control, pose uncertainty of a vehicle and some experimental issues should be solved. In this paper, following three schemes are proposed. (1) Odometry calibration scheme for the Car-Like Mobile Robot.(CLMR) (2) Accurate localization using Extended Kalman Filter(EKF) based redundant odometry fusion. (3) Trajectory tracking controller to compensate the tracking error of the CLMR. The proposed schemes are experimentally verified using a miniature Car-Like Mobile Robot. This paper shows that odometry accuracy and trajectory tracking performance can be dramatically improved by using the proposed schemes.

• Journal of Positioning, Navigation, and Timing
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• 제6권4호
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• pp.195-203
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• 2017

A 2U cube satellite called SNUGLITE has been developed by GNSS Research Laboratory in Seoul National University. Its main mission is to perform actual operation by mounting dual-frequency global positioning system (GPS) receivers. Its scientific mission aims to observe space environments and collect data. It is essential for a cube satellite to control an Earth-oriented attitude for reliable and successful data transmission and reception. To this end, an attitude estimation and control algorithm, Attitude Determination and Control System (ADCS), has been implemented in the on-board computer (OBC) processor in real time. In this paper, the Extended Kalman Filter (EKF) was employed as the attitude estimation algorithm. For the attitude control technique, the Linear Quadratic Gaussian (LQG) was utilized. The algorithm was verified through the processor in the loop simulation (PILS) procedure. To validate the ADCS algorithm in the ground, the experimental verification via a single axis Hardware-in-the-loop simulation (HILS) was used due to the simplicity and cost effectiveness, rather than using the 3-axis HILS verification (Schwartz et al. 2003) with complex air-bearing mechanism design and high cost.

• 한국정보통신학회논문지
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• 제19권4호
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• pp.925-932
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• 2015

본 논문에서는 블루투스 4.0에서부터 지원하는 BLE(BlueTooth Low Energy) 프로토콜 장치인 iBeacon을 활용하여 사용자의 위치를 측정하는 시스템에서 추정 결과를 개선하는 기법을 연구하였다. iBeacon의 상황별 신호를 시스템을 통해 측정한 뒤 수집된 데이터 분석을 통하여 간섭 요인을 분석하고 보정을 위해 EKF(Extended Kalman Filter)를 적용하였다. 필터 적용 후 데이터와 기존 데이터와 비교하고, 추정 결과의 개선을 확인하였다. 제안하는 위치추정기법은 실제 구현에 복잡성이 적고, iBeacon 기본 환경의 취약한 위치 서비스 개선에 효과적이다.