GNSS 부분 음영 지역에서 마할라노비스 거리를 이용한 GNSS/다중 IMU 센서 기반 측위 알고리즘
GNSS/Multiple IMUs Based Navigation Strategy Using the Mahalanobis Distance in Partially GNSS-denied Environments
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초록
The existing studies on the localization in the GNSS (Global Navigation Satellite System) denied environment usually exploit low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit) sensors to replace the GNSS signals. However, the navigation system still requires GNSS signals for the normal environment. This paper presents an integrated GNSS/INS (Inertial Navigation System) navigation system which combines GNSS and multiple IMU sensors using extended Kalman filter in partially GNSS-denied environments. The position and velocity of the INS and GNSS are used as the inputs to the integrated navigation system. The Mahalanobis distance is used for novelty detection to detect the outlier of GNSS measurements. When the abnormality is detected in GNSS signals, GNSS data is excluded from the fusion process. The performance of the proposed method is evaluated using MATLAB/Simulink. The simulation results show that the proposed algorithm can achieve a higher degree of positioning accuracy in the partially GNSS-denied environment.
Abstract
The existing studies on the localization in the GNSS (Global Navigation Satellite System) denied environment usually exploit low-cost MEMS IMU (Micro Electro Mechanical Systems Inertial Measurement Unit) sensors to replace the GNSS signals. However, the navigation system still requires GNSS signals for the normal environment. This paper presents an integrated GNSS/INS (Inertial Navigation System) navigation system which combines GNSS and multiple IMU sensors using extended Kalman filter in partially GNSS-denied environments. The position and velocity of the INS and GNSS are used as the inputs to the integrated navigation system. The Mahalanobis distance is used for novelty detection to detect the outlier of GNSS measurements. When the abnormality is detected in GNSS signals, GNSS data is excluded from the fusion process. The performance of the proposed method is evaluated using MATLAB/Simulink. The simulation results show that the proposed algorithm can achieve a higher degree of positioning accuracy in the partially GNSS-denied environment.
참고문헌 (16)
- S. Sasani, "Improving MEMS-IMU", GPS Integrated Systems for Land Vehicle Navigation Applications, 2016, pp.20, 89~100
- A. D. Abosekeen, "Fusion of Low-cost MEMS IMU", GPS Integrated Navigation System, 2012, 29~31
- M. Wang, "A Loosely Coupled MEMS-SINS", GNSS Integrated System for Land Vehicle Navigation in Urban Areas, 2017, 103~108
- G. Falco, "Loose and Tight GNSS", INS Integrations: Comparison of Performance Assessed in Real Urban Scenarios, 2017, pp.17(2), 255~
- L. Lou, "Sensor Fusion Based Attitude Estimation Using Low-cost MEMS-IMU for Mobile Robot Navigation", Proceedings of the 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, pp.465~468 (2011)
- M. Tanenhaus, "Miniature IMU", INS with Optimally Fused Low Drift MEMS Gyro and Accelerometers for Applications in GPS-denied Environments, ION Position Location and Navigation Symposium (PLANS), pp.2012[DOI]
- I. Skog, "qAn Open-source Multi Inertial Measurement Unit (MIMU) Platform", International Symposium on Inertial Sensors and Systems (ISISS), pp.1~4 (2014)
- A. M. Shahri, "Implementation of a Low-cost Multi-IMU by Using Information form of a Steady State Kalman Filter", AUT Journal of Electrical Engineering, 49(2), pp.195~204 (2017)
- J. B. Bancroft, "Data Fusion Algorithms for Multiple Inertial Measurement Units", Journal of Sensors, 11(12), pp.6771~6798 (2011)
- 김지연, "다중 IMU 센서와 연합형 필터 융합 기반의 GPS-free 위치 추정", 제어.로봇.시스템학회 논문지, 26(9), pp.708~714 (2020), 제어·로봇·시스템학회[DOI]
- U. N. Patel, "Sensor Fusion to Improve State Estimate Accuracy Using Multiple Inertial Measurement Units", IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) (2021)
- A. M. Sabatini, "Quaternion-based Extended Kalman Filter for Determining Orientation by Inertial and Magnetic Sensing", IEEE Transactions on Biomedical Engineering, 53(7) (2006)
- 고낙용, "무인 수상정의 융합 항법 및 GPS 이상 검출", 제어.로봇.시스템학회 논문지, 22(9), pp.723~732 (2016), 제어·로봇·시스템학회[DOI]
- P. Mahalanobis, "On the Generalized Distance in Statistics", Proceedings of the National Institute of Science, 2(1), pp.49~55 (1936)
- J. L. Marins, "An Extended Kalman Filter for Quaternion-based Orientation Estimation Using MARG Sensors", International Conference on Intelligent Robots and Systems, pp.2003~2011 (2001)
- N. Li, "Indoor and Outdoor Low-cost Seamless Integrated Navigation System Based on the Integration of INS", GNSS, Journal of Remote Sensing, pp.2020, 12(19)[DOI]