Tightly coupled SINS/GNSS integration based on simplified SSKF

Jing Zhao; Chengdong Xu; Pengfei Zhang

doi:10.3969/j.issn.1001-506X.2017.11.20

Tightly coupled SINS/GNSS integration based on simplified SSKF

Jing Zhao, Chengdong Xu, Pengfei Zhang

School of Aerospace Engineering

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

The spherical simplex Kalman filter (SSKF) used in tightly coupled SINS/GNSS integration is simplified, namely simplified SSKF, due to the linear state equation but the nonlinear observation equation. Using state prediction of common Kalman filter (FK) and observation prediction of SSKF, the generating and updating of each sigma point during state prediction is omitted in simplified SSKF, which can shorten filtering time with less accuracy loss. Through mathematical simulations, simplified SSKF is proved to be more efficient than common SSKF. Besides, simplified SSKF has almost the same position (velocity) accuracy as common SSKF.

Original language	English
Pages (from-to)	2529-2534
Number of pages	6
Journal	Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics
Volume	39
Issue number	11
DOIs	https://doi.org/10.3969/j.issn.1001-506X.2017.11.20
Publication status	Published - 1 Nov 2017

Keywords

Linear state equation
Simplification
Spherical simplex Kalman filter
Tightly coupled SINS/GNSS integration

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10.3969/j.issn.1001-506X.2017.11.20

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title = "Tightly coupled SINS/GNSS integration based on simplified SSKF",

abstract = "The spherical simplex Kalman filter (SSKF) used in tightly coupled SINS/GNSS integration is simplified, namely simplified SSKF, due to the linear state equation but the nonlinear observation equation. Using state prediction of common Kalman filter (FK) and observation prediction of SSKF, the generating and updating of each sigma point during state prediction is omitted in simplified SSKF, which can shorten filtering time with less accuracy loss. Through mathematical simulations, simplified SSKF is proved to be more efficient than common SSKF. Besides, simplified SSKF has almost the same position (velocity) accuracy as common SSKF.",

keywords = "Linear state equation, Simplification, Spherical simplex Kalman filter, Tightly coupled SINS/GNSS integration",

author = "Jing Zhao and Chengdong Xu and Pengfei Zhang",

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T1 - Tightly coupled SINS/GNSS integration based on simplified SSKF

AU - Zhao, Jing

AU - Xu, Chengdong

AU - Zhang, Pengfei

PY - 2017/11/1

Y1 - 2017/11/1

N2 - The spherical simplex Kalman filter (SSKF) used in tightly coupled SINS/GNSS integration is simplified, namely simplified SSKF, due to the linear state equation but the nonlinear observation equation. Using state prediction of common Kalman filter (FK) and observation prediction of SSKF, the generating and updating of each sigma point during state prediction is omitted in simplified SSKF, which can shorten filtering time with less accuracy loss. Through mathematical simulations, simplified SSKF is proved to be more efficient than common SSKF. Besides, simplified SSKF has almost the same position (velocity) accuracy as common SSKF.

AB - The spherical simplex Kalman filter (SSKF) used in tightly coupled SINS/GNSS integration is simplified, namely simplified SSKF, due to the linear state equation but the nonlinear observation equation. Using state prediction of common Kalman filter (FK) and observation prediction of SSKF, the generating and updating of each sigma point during state prediction is omitted in simplified SSKF, which can shorten filtering time with less accuracy loss. Through mathematical simulations, simplified SSKF is proved to be more efficient than common SSKF. Besides, simplified SSKF has almost the same position (velocity) accuracy as common SSKF.

KW - Linear state equation

KW - Simplification

KW - Spherical simplex Kalman filter

KW - Tightly coupled SINS/GNSS integration

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JF - Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics

IS - 11

ER -

Tightly coupled SINS/GNSS integration based on simplified SSKF

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