Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gai...
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Autor*in:	Liang Xue [verfasserIn] Chengyu Jiang [verfasserIn] Lixin Wang [verfasserIn] Jieyu Liu [verfasserIn] Weizheng Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	MEMS gyroscope noise reduction Kalman filter direct modeling random walk process

Übergeordnetes Werk:	In: Micromachines - MDPI AG, 2010, 6(2015), 2, Seite 266-280
Übergeordnetes Werk:	volume:6 ; year:2015 ; number:2 ; pages:266-280

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/mi6020266

Katalog-ID:	DOAJ060006595

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allfields	10.3390/mi6020266 doi (DE-627)DOAJ060006595 (DE-599)DOAJf0addef531104829a943fc82f515c517 DE-627 ger DE-627 rakwb eng TJ1-1570 Liang Xue verfasserin aut Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. MEMS gyroscope noise reduction Kalman filter direct modeling random walk process Mechanical engineering and machinery Chengyu Jiang verfasserin aut Lixin Wang verfasserin aut Jieyu Liu verfasserin aut Weizheng Yuan verfasserin aut In Micromachines MDPI AG, 2010 6(2015), 2, Seite 266-280 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:6 year:2015 number:2 pages:266-280 https://doi.org/10.3390/mi6020266 kostenfrei https://doaj.org/article/f0addef531104829a943fc82f515c517 kostenfrei http://www.mdpi.com/2072-666X/6/2/266 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 2 266-280
spelling	10.3390/mi6020266 doi (DE-627)DOAJ060006595 (DE-599)DOAJf0addef531104829a943fc82f515c517 DE-627 ger DE-627 rakwb eng TJ1-1570 Liang Xue verfasserin aut Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. MEMS gyroscope noise reduction Kalman filter direct modeling random walk process Mechanical engineering and machinery Chengyu Jiang verfasserin aut Lixin Wang verfasserin aut Jieyu Liu verfasserin aut Weizheng Yuan verfasserin aut In Micromachines MDPI AG, 2010 6(2015), 2, Seite 266-280 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:6 year:2015 number:2 pages:266-280 https://doi.org/10.3390/mi6020266 kostenfrei https://doaj.org/article/f0addef531104829a943fc82f515c517 kostenfrei http://www.mdpi.com/2072-666X/6/2/266 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 2 266-280
allfields_unstemmed	10.3390/mi6020266 doi (DE-627)DOAJ060006595 (DE-599)DOAJf0addef531104829a943fc82f515c517 DE-627 ger DE-627 rakwb eng TJ1-1570 Liang Xue verfasserin aut Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. MEMS gyroscope noise reduction Kalman filter direct modeling random walk process Mechanical engineering and machinery Chengyu Jiang verfasserin aut Lixin Wang verfasserin aut Jieyu Liu verfasserin aut Weizheng Yuan verfasserin aut In Micromachines MDPI AG, 2010 6(2015), 2, Seite 266-280 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:6 year:2015 number:2 pages:266-280 https://doi.org/10.3390/mi6020266 kostenfrei https://doaj.org/article/f0addef531104829a943fc82f515c517 kostenfrei http://www.mdpi.com/2072-666X/6/2/266 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 2 266-280
allfieldsGer	10.3390/mi6020266 doi (DE-627)DOAJ060006595 (DE-599)DOAJf0addef531104829a943fc82f515c517 DE-627 ger DE-627 rakwb eng TJ1-1570 Liang Xue verfasserin aut Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. MEMS gyroscope noise reduction Kalman filter direct modeling random walk process Mechanical engineering and machinery Chengyu Jiang verfasserin aut Lixin Wang verfasserin aut Jieyu Liu verfasserin aut Weizheng Yuan verfasserin aut In Micromachines MDPI AG, 2010 6(2015), 2, Seite 266-280 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:6 year:2015 number:2 pages:266-280 https://doi.org/10.3390/mi6020266 kostenfrei https://doaj.org/article/f0addef531104829a943fc82f515c517 kostenfrei http://www.mdpi.com/2072-666X/6/2/266 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 2 266-280
allfieldsSound	10.3390/mi6020266 doi (DE-627)DOAJ060006595 (DE-599)DOAJf0addef531104829a943fc82f515c517 DE-627 ger DE-627 rakwb eng TJ1-1570 Liang Xue verfasserin aut Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope. MEMS gyroscope noise reduction Kalman filter direct modeling random walk process Mechanical engineering and machinery Chengyu Jiang verfasserin aut Lixin Wang verfasserin aut Jieyu Liu verfasserin aut Weizheng Yuan verfasserin aut In Micromachines MDPI AG, 2010 6(2015), 2, Seite 266-280 (DE-627)665016069 (DE-600)2620864-7 2072666X nnns volume:6 year:2015 number:2 pages:266-280 https://doi.org/10.3390/mi6020266 kostenfrei https://doaj.org/article/f0addef531104829a943fc82f515c517 kostenfrei http://www.mdpi.com/2072-666X/6/2/266 kostenfrei https://doaj.org/toc/2072-666X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2015 2 266-280
language	English
source	In Micromachines 6(2015), 2, Seite 266-280 volume:6 year:2015 number:2 pages:266-280
sourceStr	In Micromachines 6(2015), 2, Seite 266-280 volume:6 year:2015 number:2 pages:266-280
format_phy_str_mv	Article
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topic_facet	MEMS gyroscope noise reduction Kalman filter direct modeling random walk process Mechanical engineering and machinery
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authorswithroles_txt_mv	Liang Xue @@aut@@ Chengyu Jiang @@aut@@ Lixin Wang @@aut@@ Jieyu Liu @@aut@@ Weizheng Yuan @@aut@@
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callnumber-first	T - Technology
author	Liang Xue
spellingShingle	Liang Xue misc TJ1-1570 misc MEMS gyroscope misc noise reduction misc Kalman filter misc direct modeling misc random walk process misc Mechanical engineering and machinery Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal
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topic_title	TJ1-1570 Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal MEMS gyroscope noise reduction Kalman filter direct modeling random walk process
topic	misc TJ1-1570 misc MEMS gyroscope misc noise reduction misc Kalman filter misc direct modeling misc random walk process misc Mechanical engineering and machinery
topic_unstemmed	misc TJ1-1570 misc MEMS gyroscope misc noise reduction misc Kalman filter misc direct modeling misc random walk process misc Mechanical engineering and machinery
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title	Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal
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title_full	Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal
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title_sort	noise reduction of mems gyroscope based on direct modeling for an angular rate signal
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title_auth	Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal
abstract	In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope.
abstractGer	In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope.
abstract_unstemmed	In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS) gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF) was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz), respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope.
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title_short	Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal
url	https://doi.org/10.3390/mi6020266 https://doaj.org/article/f0addef531104829a943fc82f515c517 http://www.mdpi.com/2072-666X/6/2/266 https://doaj.org/toc/2072-666X
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author2	Chengyu Jiang Lixin Wang Jieyu Liu Weizheng Yuan
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up_date	2024-07-04T01:42:39.182Z
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Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

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