Error Bounds of the GNSS/INS Integrated System Against GNSS Fault for Integrity Monitoring

Liu, Wei; Song, Dan; Wang, Zhipeng; Zhu, Yanbo; Li, Qiang

doi:10.33012/2020.17161

Cited by 2 publications

(4 citation statements)

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“…On the other hand, the proposed method improves the error bounding effect. The state error can be expressed by filtering innovation, as shown in Equation (25), where

{bold-italicf}_{z_{k}}

is the GNSS fault vector [24].

{bold-italicε}_{k} = - {bold-italicU}_{k} {bold-italicγ}_{k} + ({bold-italicU}_{k} + {bold-italicK}_{k}) {bold-italicV}_{k} + ({bold-italicU}_{k} + {bold-italicK}_{k}) {bold-italicf}_{z_{k}}

{bold-italicU}_{k} = {bold-italicL}_{k} {({bold-italicH}_{k}^{T} H_{k})}^{- 1} {H_{k}}^{T}

…”

Section: Dynamic Fading Filter Optimisation Under Gnss Fault Conditionsmentioning

confidence: 99%

“…However, since the use of fading filtering can only accelerate the convergence process instead of removing it completely, the current fault identification can be affected by the previous one, but with a low probability. The probability of reduction P d is given by Equation (24).…”

Section: Optimisation Of χ 2 -Mss Algorithm Based On Dynamic Fading F...mentioning

confidence: 99%

“…On the other hand, the proposed method improves the error bounding effect. The state error can be expressed by filtering innovation, as shown in Equation (25), where f z k is the GNSS fault vector [24].…”

Section: Optimisation Of χ 2 -Mss Algorithm Based On Dynamic Fading F...mentioning

confidence: 99%

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Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Wang

Zhu

et al. 2021

IET Radar Sonar & Navi

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In the application of integrity monitoring of Global Navigation Satellite System (GNSS)/Inertial Navigation System integrated navigation system in complex environments, such as vegetation covered areas, the Kalman filtering (KF) has the disadvantages of low positioning precision, poor performance of fault identification and error bounds. This study proposes an integrity monitoring method based on dynamic fading filter optimisation. According to the real‐time updates of filtering innovation, a fading filtering algorithm is designed, which can adjust the fading factor dynamically. The dynamic fading filtering algorithm is then combined with the fault identification algorithm, which integrates chi‐square and multiple solution separation. An optimisation strategy of setting a fading period to speed up the filter convergence is proposed. The simulation results show that compared with the KF, the dynamic fading filtering reduces the standard deviations of positioning errors by 26%, 39%, and 26%, respectively, in the east, north and up directions. By reducing the filter convergence period, the proposed dynamic fading filtering can enhance the accuracy of the identification results when the subsequent GNSS fault is minor. It can shorten the identification time to about 20% when the subsequent fault is large. The dynamic fading filtering can also limit the state errors below the bounds correctly.

show abstract

“…On the other hand, the proposed method improves the error bounding effect. The state error can be expressed by filtering innovation, as shown in Equation (25), where

{bold-italicf}_{z_{k}}

is the GNSS fault vector [24].

{bold-italicε}_{k} = - {bold-italicU}_{k} {bold-italicγ}_{k} + ({bold-italicU}_{k} + {bold-italicK}_{k}) {bold-italicV}_{k} + ({bold-italicU}_{k} + {bold-italicK}_{k}) {bold-italicf}_{z_{k}}

{bold-italicU}_{k} = {bold-italicL}_{k} {({bold-italicH}_{k}^{T} H_{k})}^{- 1} {H_{k}}^{T}

…”

Section: Dynamic Fading Filter Optimisation Under Gnss Fault Conditionsmentioning

confidence: 99%

Section: Optimisation Of χ 2 -Mss Algorithm Based On Dynamic Fading F...mentioning

confidence: 99%

See 1 more Smart Citation

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Wang

Zhu

et al. 2021

IET Radar Sonar & Navi

Self Cite

View full text Add to dashboard Cite

show abstract

“…For example, Pervan (1996) and Enge (1999) demonstrate navigation integrity method for aircraft precision landing using GNSS and ground‐based pseudolites, which ensure sufficient measurement redundancy. To the authors' best knowledge, the most common multi‐sensor integrity solutions for land vehicles are based on the integration of GNSS, Inertial Measurement Units (IMU), and odometer or speedometer (Han et al., 2020; W. Liu et al., 2020; El‐Mowafy & Kubo, 2017; Toledo‐Moreo et al., 2010). Maaref et al.…”

Section: Introductionmentioning

confidence: 99%

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

Gabela

Kealy²,

Hedley

et al. 2021

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Error Bounds of the GNSS/INS Integrated System Against GNSS Fault for Integrity Monitoring

Cited by 2 publications

References 0 publications

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Case study of Bayesian RAIM algorithm integrated with Spatial Feature Constraint and Fault Detection and Exclusion algorithms for multi‐sensor positioning

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