Context-Awareness Assisted PPP/INS Integrated Navigation Enhancement Algorithm

Zhang, Qieqie; Zhao, Long; Wang, Bin; Binghao, Wang

doi:10.23919/ccc52363.2021.9549913

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Vehicle experiments show that the recognition of vehicle context is beneficial for improving positioning accuracy. On this basis, they also verified the auxiliary effect of motion state recognition on PPP/INS tightly coupled integration [21]. Although the detection threshold can be adjusted online, it still requires manual se ing of the initial values.…”

Section: Existing Methodsmentioning

confidence: 65%

“…The motion constraint module could utilize the measurements of sensors and state parameters of the integration system to further construct motion constraint equations, given the vehicle motion behaviors [8,12,21]. Available vehicle positioning constraint information is aggregated in this section and the constraint conditions are divided into four categories: sensor error calibration, velocity constraint, angle constraint, and position constraint.…”

Section: Constraint Equationsmentioning

confidence: 99%

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Context Awareness Assisted Integration System for Land Vehicles

Li,

Guo,

Liu

et al. 2024

Electronics

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Accurate context awareness of land vehicles can assist integrated navigation systems. Motion behavior recognition is one context awareness of vehicles, based on which constraint information helps reduce the impact of short-term blockage of navigation signals on radio-frequency-based positioning systems. To improve the reliability of behavior recognition, we proposed a machine learning-based vehicle motion behavior recognition and constraint method (MLMRC). The machine learning-based recognition process is directly driven by raw data from low-cost MEMS-IMU, while the traditional threshold-based method relies on previous experience. Four categories of constraint information—sensor error calibration, velocity constraint, angle constraint, and position constraint—were constructed from the recognition results. Both the simulated vehicle experiment and real vehicle experiment demonstrate the performance of the MLMRC method. When there is a short-term blockage, the MLMRC method can reduce the positioning error from 17.2% to 38.3% compared with the traditional method, which effectively improves positioning accuracy and provides support for autonomous vehicles in complex urban environments.

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Section: Existing Methodsmentioning

confidence: 65%

Section: Constraint Equationsmentioning

confidence: 99%

Context Awareness Assisted Integration System for Land Vehicles

Li,

Guo,

Liu

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Concerning Sage Husa Adaptive KF (SHAKF) or robust adaptive filtering method [2][3][4][5] , measurement conditions can at least be more finely divided into "normal/trust", "suspicious/partial trust" and "abnormal/discard" categories. The size of the measurement noise variance matrix can be modified or given different measurement utilization weights for different measurement categories.…”

Section: Prefacementioning

confidence: 99%

A robust estimation method of GNSS/IMU fusion kalman filter

Pu,

Liu

2024

J. Phys.: Conf. Ser.

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Aiming at the traditional gross error detection method of GNSS/IMU fusion Kalman filter, which simply retains or eliminates abnormal observation data and does not make full use of useful observation information, a new robust estimation method of Kalman filter is proposed. Based on the chi-square statistical hypothesis test, this method constructs continuous change measurement using weight coefficient, fully excavates measurable innovation between normal value and abnormal value, and establishes a new measurement update equation of Kalman filter robust estimation. Finally, through GNSS/IMU integrated navigation simulation, the advantages of the new Kalman filter are verified: no parameter adjustment is required, and the statistical error fluctuation is smaller than that of the traditional adaptive filter.

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Tightly coupled multi-frequency PPP-RTK/INS integration model and its application in an urban environment

Zhu,

Yao,

et al. 2023

Geo-spatial Information Science

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Context-Awareness Assisted PPP/INS Integrated Navigation Enhancement Algorithm

Cited by 3 publications

References 4 publications

Context Awareness Assisted Integration System for Land Vehicles

Context Awareness Assisted Integration System for Land Vehicles

A robust estimation method of GNSS/IMU fusion kalman filter

Tightly coupled multi-frequency PPP-RTK/INS integration model and its application in an urban environment

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