• Journal of Advanced Navigation Technology
• /
• v.16 no.5
• /
• pp.738-743
• /
• 2012

Kalman filter is generally used in INS/GPS integrated navigation filter. However, the INS with low performance inertia sensor can not find accurate azimuth in initial alignment stage because sensor noise level is too large compare to Earth rotation rate, therefore the performance and stability of Kalman filter can not be guaranteed. In this paper, the extended Kalman filter and particle filter combined filter structure which can be overcome large initial azimuth error is proposed.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.1942-1943
• /
• 2011

차량의 무인화에 대한 관심이 증대하면서 자율 주행 문제의 중요성이 부각되고 있다. 현재 전역적인 차량 위치는 GPS에 도움을 받고 있지만 도심 고층 빌딩 밀집 지역에서 GPS 신호가 불안해지는 멀티 패스 페이딩 현상에 대한 대안 및 터널을 통과할 때 GPS 신호가 단절되는 구간에 대한 대안이 필요하다. 본 연구에서는 MEMS 기반의 관성 센서를 제작하고 이를 이용하여 차량의 주행 모드를 자동으로 판별한 뒤 각 상황에 알맞은 칼만 필터를 설계하여 차량 위치를 파악하는 알고리즘을 제안한다. 제안한 알고리즘은 실제 임베디드 시스템에 이식되어 10Hz로 동작함을 확인하였고 GPS 음영 지역에서 3분 이내에는 GPS 오차 범위 내에서 차량 위치를 파악할 수 있음을 실험을 통해 확인하였다.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.19 no.6
• /
• pp.666-673
• /
• 2013

This paper proposes the performance evaluation test of attitude heading reference system (AHRS) suitable for small high speed autonomous underwater vehicle(AUV). Although IMU can provides the detail attitude information, it is sometime not suitable for small AUV with short operation time in view of price and the electrical power consumption. One of alternative for tactical grade IMU is the AHRS based micro-machined electro mechanical system(MEMS) which can overcome many problems that have inhibited the adoption of inertial system for small AUV such as cost and power consumption. A cost effective and small size AHRS which incorporates measurements from 3-axis MEMS gyroscopes, accelerometers, and 3-axis magnetometers has been developed to provide a complete attitude solution for AUV and the attitude calculation algorithm is derived based the coordinate transform equation and Kalman filter. The developed AHRS was validated through various performance tests as like the magnetometer calibration, operating experiments using land mobile vehicle and flight motion simulator (FMS). The test of magnetometer calibration shows the developed MEMS AHRS is robust to the external magent field change and the test with land vehicle proves the leveling error of developed MEMS AHRS is below $0.5^{\circ}/hr$. The results of FMS test shows the fact that AHRS provides the measurement with $0.5^{\circ}/hr$ error during 5 minutes operation time. These results of performance evaluation tests showed that the developed AHRS provides attitude information which error of roll and pitch are below $1^{\circ}$ and the error of yaw is below $5^{\circ}$ and satisfies the required specification. It is expected that developed AHRS can provide the precise attitude measurement under sea trial with real AUV.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2012.10a
• /
• pp.148-150
• /
• 2012

바람이나 파도에 의해서 요트 계류장과 요트 사이 또는 요트 계류장과 계류장 사이에는 충격이 발생하는데, 이러한 충격으로 인하여 계류장 또는 요트가 파손되거나 전복된다. 특히, 방파제 없이 외해에 설치한 요트 계류장은 이러한 위기에 항상 노출되어 있어 이에 대한 대책이 시급한 실정이다. 논문에서는 요트 계류장의 롤링, 히브, 핏칭 등의 운동을 측정하기 위한 하드웨어의 구축과 평가에 관해서 기술하였다. 하드웨어는 MEMS 기반 자이로와 가속도계 등의 관성 센서를 내장한 반도체 센서 SD746을 이용하여 구축하였고, 구축한 하드웨어를 이용하여 소형 요트 계류장에서 발생할 수 있는 운동을 수작업으로 생성하여 측정 및 평가하였다. 실험결과 x-축, y-축, z-축 등 3축 가속도와 3축 각속도 측정이 가능하여 요트 계류장의 운동 상태 모니터링이 가능함을 알았다. 본 연구는 추후 요트 계류장의 위기상태를 평가하고 통보하기 위한 시스템 구축에 이용할 예정이다.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.1
• /
• pp.54-61
• /
• 2012

In this paper, the pose estimation method for the satellite GTB (Ground Test Bed) using vision/MEMS IMU (Inertial Measurement Unit) integrated system is presented. The GTB for verifying a satellite system on the ground is similar to the mobile robot having thrusters and a reaction wheel as actuators and floating on the floor by compressed air. The EKF (Extended Kalman Filter) is also used for fusion of MEMS IMU and vision system that consists of a single camera and infrared LEDs that is ceiling landmarks. The fusion filter generally utilizes the position of feature points from the image as measurement. However, this method can cause position error due to the bias of MEMS IMU when the camera image is not obtained if the bias is not properly estimated through the filter. Therefore, it is proposed that the fusion method which uses the position of feature points and the velocity of the camera determined from optical flow of feature points. It is verified by experiments that the performance of the proposed method is robust to the bias of IMU compared to the method that uses only the position of feature points.