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Adaptive robust ultra‐tightly coupled global navigation satellite system/inertial navigation system based on global positioning system/BeiDou vector tracking loops
Author(s) -
Xie Fei,
Liu Jianye,
Li Rongbing,
Hang Yijun
Publication year - 2014
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2013.0294
Subject(s) - satellite tracking , computer science , inertial navigation system , satellite system , satellite , tracking (education) , precise point positioning , satellite navigation , tracking system , global positioning system , gnss applications , inertial frame of reference , control theory (sociology) , computer vision , artificial intelligence , aerospace engineering , engineering , kalman filter , physics , control (management) , telecommunications , quantum mechanics , psychology , pedagogy
With the development of global navigation satellite system (GNSS), the GNSS/inertial navigation system (INS) integrated system offers the users better positioning or navigation performance. This paper proposes an adaptive robust ultra‐tightly coupled GNSS/INS system based on a novel vector tracking strategy for combining both global positioning system (GPS) L1 and BeiDou B1 signals’ tracking together. The inherent mechanism of the vector tracking approach has been analysed to describe the relationship between the replica signals and user's dynamic state. Then, an adaptive robust filter is used to gain the accurate estimates of vehicle states when the vehicle is under a weak‐signal or large manoeuvring environment. Finally, the experimental platform is set up using a GPS/BeiDou signal simulator and an inertial measurement unit simulator and the test results show that the proposed ultra‐tightly coupled system can keep the tracking loops from the high dynamic perturbations, which saves the cost time of signal reacquisition. Moreover, the presented adaptive robust ultra‐tightly coupled system can obtain a higher accuracy than Kalman filtering in a simultaneous weak‐signal and large manoeuvring environment.

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