• 한국항공우주학회지
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• 제32권10호
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• pp.53-59
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• 2004

저급 관성센서로 구성된 중첩 IMU의 오차 보정 기법을 제안한다. 고장 검출 및 분리 기능을 갖는 중첩 IMU의 오차 보상을 위하여 먼저 IMU 내부의 기본 좌표계를 정의하고 그 좌표계상에서 오차 모델을 유도한다. 오차 계수를 추정하기 위한 수식을 정립하고 원추 배치를 갖는 중첩 IMU의 오차 보상을 위해 2축 레이트 테이블의 시험 순시를 제시한다. 그리고 제안된 오차 보상 기법의 성능을 검증하기 위하여 저급 관성센서를 사용하여 원추 배치 중첩 IMU를 구현하고 오차 계수를 추정, 보상한다.

• ETRI Journal
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• 제27권4호
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• pp.418-426
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• 2005

A calibration technique for a redundant inertial measurement unit (IMU) containing low-grade inertial sensors is proposed. In order to calibrate a redundant IMU that can detect and isolate faulty sensors, the fundamental coordinate frames in the IMU are defined and the IMU error is modeled based on the frames. Equations to estimate the error coefficients of the redundant IMU are formulated, and a test sequence using a 2-axis turntable is also presented. Finally, a redundant IMU with cone configuration is implemented using low-grade inertial sensors, and the performance of the proposed technique is verified experimentally.

• International Journal of Aeronautical and Space Sciences
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• 제13권3호
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• pp.398-404
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• 2012

A fault detection process is necessary for high integrity systems like satellites, missiles and aircrafts. Especially, the satellite has to be expected to detect faults autonomously because it cannot be fixed by an expert in the space. Faults can cause critical errors to the entire system and the satellite does not have sufficient computation power to operate a large scale fault management system. Thus, a fault detection method, which has less computational burden, is required. In this paper, we proposed a modified PCA (Principal Component Analysis) as a powerful fault detection method of redundant IMU (Inertial Measurement Unit). The proposed method combines PCA with the parity space approach and it is much more efficient than the others. The proposed fault detection algorithm, modified PCA, is shown to outperform fault detection through a simulation example.

• 전기학회논문지
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• 제67권12호
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• pp.1648-1656
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• 2018

The configuration of redundant inertial sensors are very important when considering navigation performance and fault detection and isolation (FDI) performance. By constructing a redundant sensor system using multiple inertial sensors, it is possible to improve the navigation performance and fault detection and isolation performance, which are highly related to the sensor configuration and allocation. In order to deploy multiple MEMS inertial measurement units effectively, a configuration and allocation methods considering navigation performance, fault detection and isolation performance, and lever arm effect in one plane are presented, and the performance is analyzed through simulation in this research. From the results, it is confirmed that the proposed configuration and allocation method can improve navigation, FDI, and lever arm effect rejection performances more effectively by more than 70%.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.116.5-116
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• 2001

When inertial navigation system(INS) employ more sensors that mutually orthogonal sets to three, the redundant sensor system can have improved reliability and accuracy. For the redundant system the placement of redundant sensors is related to the system performance and also the number and proper orientation of sensors are important. We consider INS sensor configurations using two IMUs comprised mutually orthogonal sets of three. We suggest several configurations using two IMUs and analyze the system performance and the FDI(fault detection and isolation) properties from suggested configurations.

• 제어로봇시스템학회논문지
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• 제15권10호
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• pp.1002-1008
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• 2009

We consider a double faults detection and isolation problem using modified extended parity space approach for inertial measurement unit which use redundant inertial sensors. A redundant IMU which has a hardware redundant is composed of the cone shape because it is good for fault detection and isolation. We analyze the type of double faults and the reason why fault isolation performance is low. We propose modified extended parity space approach method using EPSA and the difference of sensor data.