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    RISS 인기검색어

      Accuracy improvement of navigation system Using GPS/INS data fusion

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      https://www.riss.kr/link?id=A100312851

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      다국어 초록 (Multilingual Abstract)

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      Position and heading error in navigation system has been reduced in several ways and used by some commercial navigation systems but are usually quite expensive. It is for this reason that we propose the use of Extended Kalman filter in the integration of a low cost global positioning system (GPS) and an inertial navigation system (INS). Both the GPS and INS have big errors that is why we used EKF to maximize the advantage of each individual system in calculating for the heading angle and position.
      The suggested method was tested using low-cost GPS, IMU and cheap digital compass. The results showed that an accurate heading and position information were obtained which verified the effectiveness of our algorithm.
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      Position and heading error in navigation system has been reduced in several ways and used by some commercial navigation systems but are usually quite expensive. It is for this reason that we propose the use of Extended Kalman filter in the integration...

      Position and heading error in navigation system has been reduced in several ways and used by some commercial navigation systems but are usually quite expensive. It is for this reason that we propose the use of Extended Kalman filter in the integration of a low cost global positioning system (GPS) and an inertial navigation system (INS). Both the GPS and INS have big errors that is why we used EKF to maximize the advantage of each individual system in calculating for the heading angle and position.
      The suggested method was tested using low-cost GPS, IMU and cheap digital compass. The results showed that an accurate heading and position information were obtained which verified the effectiveness of our algorithm.

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      목차 (Table of Contents)

      • Abstract
      • 1. Introduction
      • 2. Background and algorithm
      • 3. Simulation result
      • 4. Conclusion
      • Abstract
      • 1. Introduction
      • 2. Background and algorithm
      • 3. Simulation result
      • 4. Conclusion
      • References
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