An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer in...

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Autores principales:	Peihui Yan, Jinguang Jiang, Fangning Zhang, Dongpeng Xie, Jiaji Wu, Chao Zhang, Yanan Tang, Jingnan Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	integrated navigation robust adaptive Kalman filter low computation complexity real-time performance single-frequency GNSS/MEMS-IMU/odometer module low power consumption Science Q
Acceso en línea:	https://doaj.org/article/f9be30e817b8419ca1d59ea79c804ab9
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spelling	oai:doaj.org-article:f9be30e817b8419ca1d59ea79c804ab92021-11-11T18:53:47ZAn Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module10.3390/rs132143172072-4292https://doaj.org/article/f9be30e817b8419ca1d59ea79c804ab92021-10-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4317https://doaj.org/toc/2072-4292Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.Peihui YanJinguang JiangFangning ZhangDongpeng XieJiaji WuChao ZhangYanan TangJingnan LiuMDPI AGarticleintegrated navigationrobust adaptive Kalman filterlow computation complexityreal-time performancesingle-frequency GNSS/MEMS-IMU/odometer modulelow power consumptionScienceQENRemote Sensing, Vol 13, Iss 4317, p 4317 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	integrated navigation robust adaptive Kalman filter low computation complexity real-time performance single-frequency GNSS/MEMS-IMU/odometer module low power consumption Science Q
spellingShingle	integrated navigation robust adaptive Kalman filter low computation complexity real-time performance single-frequency GNSS/MEMS-IMU/odometer module low power consumption Science Q Peihui Yan Jinguang Jiang Fangning Zhang Dongpeng Xie Jiaji Wu Chao Zhang Yanan Tang Jingnan Liu An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
description	Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.
format	article
author	Peihui Yan Jinguang Jiang Fangning Zhang Dongpeng Xie Jiaji Wu Chao Zhang Yanan Tang Jingnan Liu
author_facet	Peihui Yan Jinguang Jiang Fangning Zhang Dongpeng Xie Jiaji Wu Chao Zhang Yanan Tang Jingnan Liu
author_sort	Peihui Yan
title	An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
title_short	An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
title_full	An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
title_fullStr	An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
title_full_unstemmed	An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
title_sort	improved adaptive kalman filter for a single frequency gnss/mems-imu/odometer integrated navigation module
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f9be30e817b8419ca1d59ea79c804ab9
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An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

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