Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix

Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix

To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of P matrix. Quantitative analysis of field test datasets was made to compare the navigation accurac...

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Main Authors: Qingli Li, Yalong Ban, Xiaoji Niu, Quan Zhang, Linlin Gong, Jingnan Liu
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2015/109267
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spelling doaj-5561bd0d76904565b013de5fd1e6c3562020-11-24T22:35:16ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472015-01-01201510.1155/2015/109267109267Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P MatrixQingli Li0Yalong Ban1Xiaoji Niu2Quan Zhang3Linlin Gong4Jingnan Liu5Wuhan University, No. 129, Luoyu Road, Wuhan, Hubei 430079, ChinaWuhan University, No. 129, Luoyu Road, Wuhan, Hubei 430079, ChinaWuhan University, No. 129, Luoyu Road, Wuhan, Hubei 430079, ChinaWuhan University, No. 129, Luoyu Road, Wuhan, Hubei 430079, ChinaWuhan University, No. 129, Luoyu Road, Wuhan, Hubei 430079, ChinaWuhan University, No. 129, Luoyu Road, Wuhan, Hubei 430079, ChinaTo meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of P matrix. Quantitative analysis of field test datasets was made to compare the navigation accuracy with the standard algorithm, which indicated that the degradation caused by the simplified algorithm is small enough compared to the navigation errors of the GNSS/INS system itself. Meanwhile, the computation load and time consumption of the algorithm decreased over 50% by the improved algorithm. The work has special significance for navigation applications that request low power consumption and strict real-time response, such as cellphone, wearable devices, and deeply coupled GNSS/INS systems.http://dx.doi.org/10.1155/2015/109267
collection DOAJ
language English
format Article
sources DOAJ
author Qingli Li
Yalong Ban
Xiaoji Niu
Quan Zhang
Linlin Gong
Jingnan Liu
spellingShingle Qingli Li
Yalong Ban
Xiaoji Niu
Quan Zhang
Linlin Gong
Jingnan Liu
Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix
Mathematical Problems in Engineering
author_facet Qingli Li
Yalong Ban
Xiaoji Niu
Quan Zhang
Linlin Gong
Jingnan Liu
author_sort Qingli Li
title Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix
title_short Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix
title_full Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix
title_fullStr Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix
title_full_unstemmed Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction of P Matrix
title_sort efficiency improvement of kalman filter for gnss/ins through one-step prediction of p matrix
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2015-01-01
description To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction of P matrix. Quantitative analysis of field test datasets was made to compare the navigation accuracy with the standard algorithm, which indicated that the degradation caused by the simplified algorithm is small enough compared to the navigation errors of the GNSS/INS system itself. Meanwhile, the computation load and time consumption of the algorithm decreased over 50% by the improved algorithm. The work has special significance for navigation applications that request low power consumption and strict real-time response, such as cellphone, wearable devices, and deeply coupled GNSS/INS systems.
url http://dx.doi.org/10.1155/2015/109267
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AT yalongban efficiencyimprovementofkalmanfilterforgnssinsthroughonesteppredictionofpmatrix
AT xiaojiniu efficiencyimprovementofkalmanfilterforgnssinsthroughonesteppredictionofpmatrix
AT quanzhang efficiencyimprovementofkalmanfilterforgnssinsthroughonesteppredictionofpmatrix
AT linlingong efficiencyimprovementofkalmanfilterforgnssinsthroughonesteppredictionofpmatrix
AT jingnanliu efficiencyimprovementofkalmanfilterforgnssinsthroughonesteppredictionofpmatrix
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