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Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System
by
Sun, Junren
, Niu, Zun
, Zhu, Bocheng
in
Algorithms
/ Bandwidths
/ bds
/ deep integration
/ Estimates
/ fault detection and exclusion
/ Global positioning systems
/ GPS
/ ins
/ Navigation systems
/ Noise
/ Propagation
/ raim
/ Software
/ Values
/ vector tracking loop
2020
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Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System
by
Sun, Junren
, Niu, Zun
, Zhu, Bocheng
in
Algorithms
/ Bandwidths
/ bds
/ deep integration
/ Estimates
/ fault detection and exclusion
/ Global positioning systems
/ GPS
/ ins
/ Navigation systems
/ Noise
/ Propagation
/ raim
/ Software
/ Values
/ vector tracking loop
2020
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Do you wish to request the book?
Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System
by
Sun, Junren
, Niu, Zun
, Zhu, Bocheng
in
Algorithms
/ Bandwidths
/ bds
/ deep integration
/ Estimates
/ fault detection and exclusion
/ Global positioning systems
/ GPS
/ ins
/ Navigation systems
/ Noise
/ Propagation
/ raim
/ Software
/ Values
/ vector tracking loop
2020
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Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System
Journal Article
Fault Detection and Exclusion Method for a Deeply Integrated BDS/INS System
2020
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Overview
The Inertial Navigation System (INS) is often fused with the Global Navigation Satellite System (GNSS) to provide more robust and superior navigation service, especially in degraded signal environments. Compared with loosely and tightly coupled architectures, the Deep Integration (DI) architecture has better tracking and positioning performance. Information is shared among channels, and the assistant information from INS helps to reduce the dynamic stress of tracking loops. However, this vector tracking architecture may result in easy propagation of errors among tracking channels. To solve this problem, a Fault Detection and Exclusion (FDE) method for the deeply integrated BeiDou Navigation Satellite System (BDS)/INS navigation system is proposed in this paper. This method utilizes pre-filters’ outputs and integration filter’s estimations to form test statistics. These statistics can help to detect and exclude both step errors and Slowly Growing Errors (SGEs) correctly. The monitoring capability of the method was verified by a simulation which was based on a software receiver. The simulation results show that the proposed FDE method works effectively. Additionally, the method is convenient to be implemented in real-time applications because of its simplicity.
Publisher
MDPI AG,MDPI
Subject
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