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Extended Kalman Filtering for I.M.U. using MEMs Sensors

반도체 센서의 확장칼만필터를 이용한 자세추정

  • 전용호 (중원대학교 메카트로닉스학과)
  • Received : 2015.03.10
  • Accepted : 2015.04.23
  • Published : 2015.04.30

Abstract

This paper describes about the method for designing an extended Kalman filter to accurately measure the position of the spatial-phase system using a semiconductor sensor. Spatial position is expressed by the correlation of the rotated coordinate system attached to the body from the inertia coordinate system (a fixed coordinate system). To express the attitude, quaternion was adapted as a state variable, Then, the state changes were estimated from the input value which was measured in the gyro sensor. The observed data is the value obtained from the acceleration sensor. By matching between the measured value in the acceleration sensor and the predicted calculation value, the best variable was obtained. To increase the accuracy of estimation, designation of the extended Kalman filter was performed, which showed excellent ability to adjust the estimation period relative to the sensor property. As a result, when a three-axis gyro sensor and a three-axis acceleration sensor were adapted in the estimator, the RMS(Root Mean Square) estimation error in simulation was retained less than 1.7[$^{\circ}$], and the estimator displayed good property on the prediction of the state in 100 ms measurement period.

본 논문은 반도체 센서를 이용하여 공간상 시스템의 자세를 정확히 측정할 수 있도록 확장 칼만 필터를 설계하는 방법에 관한 연구이다. 공간상 자세는 관성좌표계(고정 좌표계)로부터 몸체에 부착된 회전좌표계의 상호 관계로 표현한다. 자세를 표현하는데 있어서 간결한 방법인 쿼터니언을 상태변수로 이용하며, 속도 센서로부터 계측된 값을 입력으로 가정하고, 상태 변화를 추정하였다. 그리고 가속도 센서로부터 획득된 값을 관측 데이터로 하여 추정한 값과의 정합과정을 통해 최적의 추정치를 얻어낸다. 이때 추정의 정밀도를 높이기 위해 추정 주기를 센서특성에 맞춰 조절하도록 확장 칼만 필터를 설계하였다. 그 결과, 3축 속도 센서와 3축 가속도 센서를 이용하여 설계된 추정기의 RMS(: Root Mean Square) 추정오차가 시뮬레이션에서 약 1.7 [$^{\circ}$] 이하로 유지되었고, 실험에서 100 [ms] 의 주기로 상태추정을 함으로 추정기가 유용함을 입증하였다.

Keywords

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