Estimation of IMU orientation using Nesterov’s accelerated gradient improved by fuzzy control rule

•The iterative approach adopts Nesterov’s accelerated gradient speeds up convergence.•The use of fuzzy control rule improves the accuracy of estimating IMU orientation.•This algorithm is able to handle the disturbance of magnetic field and non-gravity acceleration. [Display omitted] The application...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Sensors and actuators. A. Physical. Ročník 332; s. 113062
Hlavní autoři:	Tang, Liang, Fan, Yanfeng, Ye, Fangping, Lu, Wenzheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Lausanne Elsevier B.V 01.12.2021 Elsevier BV
Témata:	Accelerometers Algorithms Estimating techniques Fuzzy control Fuzzy logic Gyroscopes Inertial measurement unit Inertial platforms Kalman filters Magnetic interference suppression Magnetometers Measurement MEMS sensor Motion acceleration suppression Motion sensors Nesterov’s accelerated gradient Orientation Pitch (inclination) Robustness (mathematics) Rolling motion Sensors Yaw Inertial measurement unit Nesterov’s accelerated gradient Motion acceleration suppression MEMS sensor Magnetic interference suppression
ISSN:	0924-4247, 1873-3069
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	•The iterative approach adopts Nesterov’s accelerated gradient speeds up convergence.•The use of fuzzy control rule improves the accuracy of estimating IMU orientation.•This algorithm is able to handle the disturbance of magnetic field and non-gravity acceleration. [Display omitted] The application fields put forward higher requirements for the core technology of inertial measurement unit, namely orientation estimation. Generally, estimation of inertial measurement unit orientation is carried out by the fusion of different sensors including geomagnetic sensor and motion sensor, but in some specific environments, the field of magnetic is chaotic and inertial measurement unit itself has the interference of acceleration, orientation estimation algorithm for inertial measurement unit is required to have higher accuracy and stronger robustness. In this work a new approach is presented for estimation of inertial measurement unit orientation. The approach is based on inertial measurement unit containing gyroscope, accelerometer and magnetometer, and a fusion algorithm using Nesterov’s accelerated gradient improved by fuzzy control rule is conducted to estimate inertial measurement unit orientation. Magnetometer is used for measuring the yaw angle, whereas gyroscope, accelerometer provide information for roll and pitch angles. The experimental platform is constructed using motion sensor MPU6050 and magnetometer IST8310, the accuracy and robustness of this approach using Nesterov’s accelerated gradient improved by fuzzy control rule are vertified by four groups of comparative experiments. Experimental results indicate that compared with complementary filter and gradient descent, this approach shows an improvement in estimation of inertial measurement unit orientation.
AbstractList	The application fields put forward higher requirements for the core technology of inertial measurement unit, namely orientation estimation. Generally, estimation of inertial measurement unit orientation is carried out by the fusion of different sensors including geomagnetic sensor and motion sensor, but in some specific environments, the field of magnetic is chaotic and inertial measurement unit itself has the interference of acceleration, orientation estimation algorithm for inertial measurement unit is required to have higher accuracy and stronger robustness. In this work a new approach is presented for estimation of inertial measurement unit orientation. The approach is based on inertial measurement unit containing gyroscope, accelerometer and magnetometer, and a fusion algorithm using Nesterov's accelerated gradient improved by fuzzy control rule is conducted to estimate inertial measurement unit orientation. Magnetometer is used for measuring the yaw angle, whereas gyroscope, accelerometer provide information for roll and pitch angles. The experimental platform is constructed using motion sensor MPU6050 and magnetometer IST8310, the accuracy and robustness of this approach using Nesterov's accelerated gradient improved by fuzzy control rule are vertified by four groups of comparative experiments. Experimental results indicate that compared with complementary filter and gradient descent, this approach shows an improvement in estimation of inertial measurement unit orientation. •The iterative approach adopts Nesterov’s accelerated gradient speeds up convergence.•The use of fuzzy control rule improves the accuracy of estimating IMU orientation.•This algorithm is able to handle the disturbance of magnetic field and non-gravity acceleration. [Display omitted] The application fields put forward higher requirements for the core technology of inertial measurement unit, namely orientation estimation. Generally, estimation of inertial measurement unit orientation is carried out by the fusion of different sensors including geomagnetic sensor and motion sensor, but in some specific environments, the field of magnetic is chaotic and inertial measurement unit itself has the interference of acceleration, orientation estimation algorithm for inertial measurement unit is required to have higher accuracy and stronger robustness. In this work a new approach is presented for estimation of inertial measurement unit orientation. The approach is based on inertial measurement unit containing gyroscope, accelerometer and magnetometer, and a fusion algorithm using Nesterov’s accelerated gradient improved by fuzzy control rule is conducted to estimate inertial measurement unit orientation. Magnetometer is used for measuring the yaw angle, whereas gyroscope, accelerometer provide information for roll and pitch angles. The experimental platform is constructed using motion sensor MPU6050 and magnetometer IST8310, the accuracy and robustness of this approach using Nesterov’s accelerated gradient improved by fuzzy control rule are vertified by four groups of comparative experiments. Experimental results indicate that compared with complementary filter and gradient descent, this approach shows an improvement in estimation of inertial measurement unit orientation.
ArticleNumber	113062
Author	Ye, Fangping Lu, Wenzheng Fan, Yanfeng Tang, Liang
Author_xml	– sequence: 1 givenname: Liang surname: Tang fullname: Tang, Liang – sequence: 2 givenname: Yanfeng orcidid: 0000-0001-9312-5838 surname: Fan fullname: Fan, Yanfeng – sequence: 3 givenname: Fangping surname: Ye fullname: Ye, Fangping email: yefangping@hbut.edu.cn – sequence: 4 givenname: Wenzheng surname: Lu fullname: Lu, Wenzheng
BookMark	eNp9kM9OAjEQhxuDiYA-gLcmnhf7B7a78WQIKgnqRc5NaWdJybLFtksCJ1_D1_NJLFlPHjh1Mv197cw3QL3GNYDQLSUjSmh-vxmFRo0YYXREKSc5u0B9WgiepbrsoT4p2Tgbs7G4QoMQNoQQzoXoIzML0W5VtK7BrsLz1yV23kITu1YbbLPGbxAieLf_-foOWGkNNXgVweC1V-YUxna7S_epszrgqj0eD1i7JnpXY9_WcI0uK1UHuPk7h2j5NPuYvmSL9-f59HGRaTaZxKykXJUFFFQwU2pmSEEAGDclrGheVJWAgpF8rCZEM1FpxkyK8VxwAComVPMhuuveTcN8tmlouXGtb9KXkuWME14yUaQU7VLauxA8VHLnkwJ_kJTIk0y5kUmmPMmUnczEiH-Mtp2i6JWtz5IPHQlp8b0FL4NOyjQY60FHaZw9Q_8CGN6SeQ
CitedBy_id	crossref_primary_10_1016_j_measurement_2025_117445 crossref_primary_10_1016_j_sna_2023_114726 crossref_primary_10_1109_ACCESS_2025_3592026
Cites_doi	10.5302/J.ICROS.2017.17.0081 10.1109/TIM.2017.2668558 10.1177/0954410017723359 10.1109/TSP.2020.2988614 10.1049/iet-rsn.2019.0359 10.1134/S1064230712060147 10.1115/1.4007122 10.1007/s12555-016-0498-4 10.1016/j.sigpro.2016.10.012 10.1109/JSEN.2020.3010367 10.1109/JMEMS.2016.2564499 10.1016/j.ymssp.2019.04.064 10.1007/s11018-015-0710-6 10.1109/JSEN.2016.2589660 10.1109/TMECH.2015.2509783 10.1016/j.ast.2012.11.004 10.1016/j.measurement.2018.05.019 10.3103/S0025654419020031 10.1109/TAC.2008.923738 10.1016/j.compag.2016.12.021
ContentType	Journal Article
Copyright	2021 Elsevier B.V. Copyright Elsevier BV Dec 1, 2021
Copyright_xml	– notice: 2021 Elsevier B.V. – notice: Copyright Elsevier BV Dec 1, 2021
DBID	AAYXX CITATION 7TB 7U5 8FD FR3 L7M
DOI	10.1016/j.sna.2021.113062
DatabaseName	CrossRef Mechanical & Transportation Engineering Abstracts Solid State and Superconductivity Abstracts Technology Research Database Engineering Research Database Advanced Technologies Database with Aerospace
DatabaseTitle	CrossRef Solid State and Superconductivity Abstracts Engineering Research Database Technology Research Database Mechanical & Transportation Engineering Abstracts Advanced Technologies Database with Aerospace
DatabaseTitleList	Solid State and Superconductivity Abstracts
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering
EISSN	1873-3069
ExternalDocumentID	10_1016_j_sna_2021_113062 S0924424721005276
GroupedDBID	--K --M -~X .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARLI AAXUO ABMAC ABNEU ABYKQ ACDAQ ACFVG ACGFS ACIWK ACRLP ADBBV ADECG ADEZE ADTZH AEBSH AECPX AEKER AFKWA AFTJW AFZHZ AGHFR AGUBO AGYEJ AHHHB AHJVU AIEXJ AIKHN AITUG AIVDX AJOXV AJSZI ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FLBIZ FNPLU FYGXN G-Q GBLVA IHE J1W JJJVA KOM LY7 M36 M41 MO0 N9A O-L O9- OAUVE OGIMB OZT P-8 P-9 P2P PC. Q38 RNS ROL RPZ SDF SDG SDP SES SPC SPCBC SPD SSK SSQ SST SSZ T5K TN5 YK3 ~G- 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ADMUD ADNMO AEIPS AFJKZ AGQPQ AIIUN AJQLL ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HMU HVGLF HZ~ R2- SCB SCH SET SEW WUQ ~HD 7TB 7U5 8FD FR3 L7M
ID	FETCH-LOGICAL-c255t-913a98e8172d9c2d080ee23d9eb168ff7e82064a50c27fc22dd9c3673ee1751c3
ISICitedReferencesCount	3
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000700613100005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0924-4247
IngestDate	Sun Nov 09 06:31:41 EST 2025 Sat Nov 29 07:14:00 EST 2025 Tue Nov 18 21:43:21 EST 2025 Fri Feb 23 02:46:41 EST 2024
IsPeerReviewed	true
IsScholarly	true
Keywords	Inertial measurement unit Nesterov’s accelerated gradient Motion acceleration suppression MEMS sensor Magnetic interference suppression
Language	English
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c255t-913a98e8172d9c2d080ee23d9eb168ff7e82064a50c27fc22dd9c3673ee1751c3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0001-9312-5838
PQID	2623039278
PQPubID	2045401
ParticipantIDs	proquest_journals_2623039278 crossref_primary_10_1016_j_sna_2021_113062 crossref_citationtrail_10_1016_j_sna_2021_113062 elsevier_sciencedirect_doi_10_1016_j_sna_2021_113062
PublicationCentury	2000
PublicationDate	2021-12-01 2021-12-00 20211201
PublicationDateYYYYMMDD	2021-12-01
PublicationDate_xml	– month: 12 year: 2021 text: 2021-12-01 day: 01
PublicationDecade	2020
PublicationPlace	Lausanne
PublicationPlace_xml	– name: Lausanne
PublicationTitle	Sensors and actuators. A. Physical.
PublicationYear	2021
Publisher	Elsevier B.V Elsevier BV
Publisher_xml	– name: Elsevier B.V – name: Elsevier BV
References	Khan, Tahir, Richardson (bib23) 2016; 52 Mahony, Hamel, Pflimlin (bib9) 2008; 53 Zhuravlev, Klimov, Plotnikov (bib17) 2019; 54 Peng, Zhang, Li (bib24) 2014; 34 Lee, Jeon (bib25) 2017; 32 Zhang, Xu, Wang (bib15) 2018; 232 Wang, Qu, Zhang, Zhu, Fang (bib5) 2017; 66 Wu, Zhou, Chen, Fourati, Li (bib8) 2016; 16 Antsev, Averkiev, Petukhov (bib6) 2015; 58 Veremeenko, Savel’ev (bib2) 2013; 52 Zhang, Liao (bib13) 2017; 134 Lee, Vukovich, Lee (bib14) 2017; 15 Li, De Wagter, de Visser, Chu, de Croon (bib11) 2019; 11 Wilson, Eberle, Hayashi, Madgwick, McGregor, Jing, Vaidyanathan (bib19) 2019; 130 Xu, Dogancay, Hmam (bib20) 2017; 133 Lu, Wang, Liu (bib4) 2018; 125 Wang, Cui, Guo (bib16) 2013; 28 Su, Boyd, Candes (bib22) 2014; 3 Guo, Liu, Wang (bib10) 2017; 34 Yang, Sun (bib7) 2018; 173 Hatamleh, Ma, Flores-Abad, Xie (bib1) 2013; 135 Makni, Fourati, Kibangou (bib12) 2016; 21 Florea, Vorobyov (bib21) 2020; 68 Yang, Xing, Liu, Qian, Qian, Xue, Zhu (bib3) 2020; 50 Rahgoshay, Karimaghaie (bib18) 2020; 14 Yang (10.1016/j.sna.2021.113062_bib7) 2018; 173 Makni (10.1016/j.sna.2021.113062_bib12) 2016; 21 Peng (10.1016/j.sna.2021.113062_bib24) 2014; 34 Su (10.1016/j.sna.2021.113062_bib22) 2014; 3 Wang (10.1016/j.sna.2021.113062_bib16) 2013; 28 Zhuravlev (10.1016/j.sna.2021.113062_bib17) 2019; 54 Lu (10.1016/j.sna.2021.113062_bib4) 2018; 125 Xu (10.1016/j.sna.2021.113062_bib20) 2017; 133 Rahgoshay (10.1016/j.sna.2021.113062_bib18) 2020; 14 Mahony (10.1016/j.sna.2021.113062_bib9) 2008; 53 Yang (10.1016/j.sna.2021.113062_bib3) 2020; 50 Wilson (10.1016/j.sna.2021.113062_bib19) 2019; 130 Veremeenko (10.1016/j.sna.2021.113062_bib2) 2013; 52 Li (10.1016/j.sna.2021.113062_bib11) 2019; 11 Wu (10.1016/j.sna.2021.113062_bib8) 2016; 16 Hatamleh (10.1016/j.sna.2021.113062_bib1) 2013; 135 Lee (10.1016/j.sna.2021.113062_bib25) 2017; 32 Florea (10.1016/j.sna.2021.113062_bib21) 2020; 68 Antsev (10.1016/j.sna.2021.113062_bib6) 2015; 58 Wang (10.1016/j.sna.2021.113062_bib5) 2017; 66 Zhang (10.1016/j.sna.2021.113062_bib15) 2018; 232 Guo (10.1016/j.sna.2021.113062_bib10) 2017; 34 Zhang (10.1016/j.sna.2021.113062_bib13) 2017; 134 Lee (10.1016/j.sna.2021.113062_bib14) 2017; 15 Khan (10.1016/j.sna.2021.113062_bib23) 2016; 52
References_xml	– volume: 52 start-page: 106 year: 2013 end-page: 116 ident: bib2 article-title: In-flight alignment of a strapdown inertial navigation system of an unmanned aerial vehicle publication-title: J. Comput. Syst. Sci. Int. – volume: 232 start-page: 3024 year: 2018 end-page: 3031 ident: bib15 article-title: Spacecraft attitude estimation based on matrix Kalman filter and recursive cubature Kalman filter publication-title: Proc. Inst. Mech. Eng. Part G – J. Aerosp. Eng. – volume: 52 start-page: 761 year: 2016 end-page: 769 ident: bib23 article-title: Use of self-calibration data for multifunctional MEMS sensor prognostics publication-title: J. Microelectromech. Syst. – volume: 125 start-page: 471 year: 2018 end-page: 475 ident: bib4 article-title: An integrated accelerometer for dynamic motion systems publication-title: Measurement – volume: 14 start-page: 407 year: 2020 end-page: 414 ident: bib18 article-title: Robust in-field estimation and calibration approach for str-apdown inertial navigation systems accelerometers bias acting on the vertical channel publication-title: IET Radar Sonar Navig. – volume: 134 start-page: 19 year: 2017 end-page: 26 ident: bib13 article-title: Attitude measure system based on extended Kalman filter for multi-rotors publication-title: Comput. Electron. Agric. – volume: 32 start-page: 1036 year: 2017 end-page: 1042 ident: bib25 article-title: Ellipsoid-fitting magnetometer calibration method considering misalign-ment and soft-iron distortion publication-title: J. Inst. Control Robot. Syst. – volume: 173 start-page: 279 year: 2018 end-page: 291 ident: bib7 article-title: A fuzzy complementary Kalman filter based on visual and IMU data for UAV landing publication-title: Meas. Tech. – volume: 66 start-page: 1743 year: 2017 end-page: 1750 ident: bib5 article-title: A fast calibration method for magnetometer array and the application of ferromagnetic target localization publication-title: IEEE Trans. Instrum. Meas. – volume: 68 start-page: 3033 year: 2020 end-page: 3048 ident: bib21 article-title: A generalized accelerated composite gradient method: uniting Ne-sterov’s fast gradient method and FISTA publication-title: IEEE Trans. Signal Process. – volume: 21 start-page: 1366 year: 2016 end-page: 1375 ident: bib12 article-title: Energy-aware adaptive attitude estimation under external acceleration for pedestrian navigation publication-title: IEEE/ASME Trans. Mech. – volume: 28 start-page: 242 year: 2013 end-page: 248 ident: bib16 article-title: INS/VisNav/GPS relative navigation system for UAV publication-title: Aerosp. Sci. Technol. – volume: 133 start-page: 64 year: 2017 end-page: 78 ident: bib20 article-title: Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking publication-title: Signal Process. – volume: 53 start-page: 1203 year: 2008 end-page: 1218 ident: bib9 article-title: Nonlinear complementary filters on the special orthogonal group publication-title: IEEE Trans. Autom. Control – volume: 15 start-page: 2161 year: 2017 end-page: 2173 ident: bib14 article-title: Robust unscented kalman filter for nanosat attitude Estimation publication-title: Int. J. Control Autom. Syst. – volume: 135 year: 2013 ident: bib1 article-title: Development of a special inertial measurement unit for UAV applications publication-title: J. Dyn. Syst. Meas. Control – Trans. Asme – volume: 3 start-page: 2510 year: 2014 end-page: 2518 ident: bib22 article-title: A differential equation for modeling Nesterov’s accelerated gra-dient method: theory and insights publication-title: J. Mach. Learn. Res. – volume: 16 start-page: 6997 year: 2016 end-page: 7007 ident: bib8 article-title: Fast complementary filter for attitude estimation using low-cost MARG sensors publication-title: IEEE Sens. J. – volume: 34 start-page: 544 year: 2014 end-page: 554 ident: bib24 article-title: Field calibration of three-axis MEMS digital acceleration publication-title: J. Vib. – volume: 34 start-page: 680 year: 2017 end-page: 687 ident: bib10 article-title: Gradient descent algorithm for small UAV parameter estimation system publication-title: Trans. Nanjing Univ. Aeronaut. Astronaut. – volume: 50 start-page: 14950 year: 2020 end-page: 14957 ident: bib3 article-title: Robust navigation method for wearable human-machine interaction system based on deep learning publication-title: IEEE Sens. J. – volume: 58 start-page: 332 year: 2015 end-page: 335 ident: bib6 article-title: A nonlinear compensator of the magnetic interference of an aviation magnetometer system publication-title: Meas. Tech. – volume: 11 year: 2019 ident: bib11 article-title: In-flight model parameter and state estimation using gradient descent for high-speed flight publication-title: Int. J. Micro Air Veh. – volume: 130( start-page: 183 year: 2019 end-page: 200 ident: bib19 article-title: Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation publication-title: Mech. Syst. Signal Process. – volume: 54 start-page: 400 year: 2019 end-page: 411 ident: bib17 article-title: Usage of computer mechanics in the theory of inertial navigation systems publication-title: J. Mech. Phys. Solids – volume: 32 start-page: 1036 year: 2017 ident: 10.1016/j.sna.2021.113062_bib25 article-title: Ellipsoid-fitting magnetometer calibration method considering misalign-ment and soft-iron distortion publication-title: J. Inst. Control Robot. Syst. doi: 10.5302/J.ICROS.2017.17.0081 – volume: 66 start-page: 1743 year: 2017 ident: 10.1016/j.sna.2021.113062_bib5 article-title: A fast calibration method for magnetometer array and the application of ferromagnetic target localization publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2017.2668558 – volume: 232 start-page: 3024 year: 2018 ident: 10.1016/j.sna.2021.113062_bib15 article-title: Spacecraft attitude estimation based on matrix Kalman filter and recursive cubature Kalman filter publication-title: Proc. Inst. Mech. Eng. Part G – J. Aerosp. Eng. doi: 10.1177/0954410017723359 – volume: 34 start-page: 544 year: 2014 ident: 10.1016/j.sna.2021.113062_bib24 article-title: Field calibration of three-axis MEMS digital acceleration publication-title: J. Vib. – volume: 68 start-page: 3033 year: 2020 ident: 10.1016/j.sna.2021.113062_bib21 article-title: A generalized accelerated composite gradient method: uniting Ne-sterov’s fast gradient method and FISTA publication-title: IEEE Trans. Signal Process. doi: 10.1109/TSP.2020.2988614 – volume: 14 start-page: 407 year: 2020 ident: 10.1016/j.sna.2021.113062_bib18 article-title: Robust in-field estimation and calibration approach for str-apdown inertial navigation systems accelerometers bias acting on the vertical channel publication-title: IET Radar Sonar Navig. doi: 10.1049/iet-rsn.2019.0359 – volume: 52 start-page: 106 year: 2013 ident: 10.1016/j.sna.2021.113062_bib2 article-title: In-flight alignment of a strapdown inertial navigation system of an unmanned aerial vehicle publication-title: J. Comput. Syst. Sci. Int. doi: 10.1134/S1064230712060147 – volume: 135 year: 2013 ident: 10.1016/j.sna.2021.113062_bib1 article-title: Development of a special inertial measurement unit for UAV applications publication-title: J. Dyn. Syst. Meas. Control – Trans. Asme doi: 10.1115/1.4007122 – volume: 15 start-page: 2161 year: 2017 ident: 10.1016/j.sna.2021.113062_bib14 article-title: Robust unscented kalman filter for nanosat attitude Estimation publication-title: Int. J. Control Autom. Syst. doi: 10.1007/s12555-016-0498-4 – volume: 133 start-page: 64 year: 2017 ident: 10.1016/j.sna.2021.113062_bib20 article-title: Distributed pseudolinear estimation and UAV path optimization for 3D AOA target tracking publication-title: Signal Process. doi: 10.1016/j.sigpro.2016.10.012 – volume: 50 start-page: 14950 year: 2020 ident: 10.1016/j.sna.2021.113062_bib3 article-title: Robust navigation method for wearable human-machine interaction system based on deep learning publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2020.3010367 – volume: 52 start-page: 761 year: 2016 ident: 10.1016/j.sna.2021.113062_bib23 article-title: Use of self-calibration data for multifunctional MEMS sensor prognostics publication-title: J. Microelectromech. Syst. doi: 10.1109/JMEMS.2016.2564499 – volume: 130( start-page: 183 year: 2019 ident: 10.1016/j.sna.2021.113062_bib19 article-title: Formulation of a new gradient descent MARG orientation algorithm: Case study on robot teleoperation publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2019.04.064 – volume: 58 start-page: 332 year: 2015 ident: 10.1016/j.sna.2021.113062_bib6 article-title: A nonlinear compensator of the magnetic interference of an aviation magnetometer system publication-title: Meas. Tech. doi: 10.1007/s11018-015-0710-6 – volume: 173 start-page: 279 year: 2018 ident: 10.1016/j.sna.2021.113062_bib7 article-title: A fuzzy complementary Kalman filter based on visual and IMU data for UAV landing publication-title: Meas. Tech. – volume: 11 year: 2019 ident: 10.1016/j.sna.2021.113062_bib11 article-title: In-flight model parameter and state estimation using gradient descent for high-speed flight publication-title: Int. J. Micro Air Veh. – volume: 3 start-page: 2510 year: 2014 ident: 10.1016/j.sna.2021.113062_bib22 article-title: A differential equation for modeling Nesterov’s accelerated gra-dient method: theory and insights publication-title: J. Mach. Learn. Res. – volume: 16 start-page: 6997 year: 2016 ident: 10.1016/j.sna.2021.113062_bib8 article-title: Fast complementary filter for attitude estimation using low-cost MARG sensors publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2016.2589660 – volume: 34 start-page: 680 year: 2017 ident: 10.1016/j.sna.2021.113062_bib10 article-title: Gradient descent algorithm for small UAV parameter estimation system publication-title: Trans. Nanjing Univ. Aeronaut. Astronaut. – volume: 21 start-page: 1366 year: 2016 ident: 10.1016/j.sna.2021.113062_bib12 article-title: Energy-aware adaptive attitude estimation under external acceleration for pedestrian navigation publication-title: IEEE/ASME Trans. Mech. doi: 10.1109/TMECH.2015.2509783 – volume: 28 start-page: 242 year: 2013 ident: 10.1016/j.sna.2021.113062_bib16 article-title: INS/VisNav/GPS relative navigation system for UAV publication-title: Aerosp. Sci. Technol. doi: 10.1016/j.ast.2012.11.004 – volume: 125 start-page: 471 year: 2018 ident: 10.1016/j.sna.2021.113062_bib4 article-title: An integrated accelerometer for dynamic motion systems publication-title: Measurement doi: 10.1016/j.measurement.2018.05.019 – volume: 54 start-page: 400 year: 2019 ident: 10.1016/j.sna.2021.113062_bib17 article-title: Usage of computer mechanics in the theory of inertial navigation systems publication-title: J. Mech. Phys. Solids doi: 10.3103/S0025654419020031 – volume: 53 start-page: 1203 year: 2008 ident: 10.1016/j.sna.2021.113062_bib9 article-title: Nonlinear complementary filters on the special orthogonal group publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2008.923738 – volume: 134 start-page: 19 year: 2017 ident: 10.1016/j.sna.2021.113062_bib13 article-title: Attitude measure system based on extended Kalman filter for multi-rotors publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2016.12.021
SSID	ssj0003377
Score	2.3729215
Snippet	•The iterative approach adopts Nesterov’s accelerated gradient speeds up convergence.•The use of fuzzy control rule improves the accuracy of estimating IMU... The application fields put forward higher requirements for the core technology of inertial measurement unit, namely orientation estimation. Generally,...
SourceID	proquest crossref elsevier
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	113062
SubjectTerms	Accelerometers Algorithms Estimating techniques Fuzzy control Fuzzy logic Gyroscopes Inertial measurement unit Inertial platforms Kalman filters Magnetic interference suppression Magnetometers Measurement MEMS sensor Motion acceleration suppression Motion sensors Nesterov’s accelerated gradient Orientation Pitch (inclination) Robustness (mathematics) Rolling motion Sensors Yaw
Title	Estimation of IMU orientation using Nesterov’s accelerated gradient improved by fuzzy control rule
URI	https://dx.doi.org/10.1016/j.sna.2021.113062 https://www.proquest.com/docview/2623039278
Volume	332
WOSCitedRecordID	wos000700613100005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-3069 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003377 issn: 0924-4247 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Jj9MwFLZKhwMcEKsYGJAPCCSqVK2dNM6xQh0xqBQkWtQ5WY7jDB2VtKSLZvo_-L88L1mmggoOXKLqxWkrv89vy1sQeiWFCpNuHHgyTKXnSyG9yCexRxKwPkSQxGFs-swOw9GITafR50bjZ1ELs52HWcaurqLlf2U10IDZunT2H9hdfikQ4DMwHa7Adrj-FeMHcGi_l4bg2cdJa5HPXIVR1tqY2MDI9EdYbE20XkgJuke3jEhaF7lJAVvr6km4b83TdLPbXZdJ7flmfiN96As4wnpij-n6qstR9PyedqvfNnN1NAbaVXjASpYhYPKiRI4NwZ6LLFUV9dymHcO65ayiDjcmJ1Blu2_FWhexIN297I-qlOZrPRxJfM8ntvtmW1lhzELqgUsT1aU1deFQK2-7v9UCNiBx2V5lurMU6erBNR0n9G903B594qeT4ZCPB9Px6-UPTw8j0y_t3WSWW-iIhEHEmuiofzaYfihVPKVmpGf5p4vX5SZxcO9X_2Tw7Kl-Y8-M76N7zhHBfQugB6ihsofobq095SMUV1DCixQDlHANSthACRdQerPCNSDhAki4ABKOr7EBEnZAwhpIj9HkdDB-995zMzk8Cc7nWidqiIgpBnZvEkmSgMOhFKFJBDq_x9I0VHoggC-CjiRw-glJYBnthVQpMFS7kj5BzWyRqacIxwnzRY_2FBPgNtBAiEDGfsoE1VRJj1Gn2DguXcN6PTdlzovMxEsOe831XnO718fobfnI0nZrObTYL7jBnblpzUgOODr02EnBOe6O_YoT8CI64GqE7Nnh28_RnepInKDmOt-oF-i23K5nq_ylw9kvVeOqaA
linkProvider	Elsevier
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Estimation+of+IMU+orientation+using+Nesterov%27s+accelerated+gradient+improved+by+fuzzy+control+rule&rft.jtitle=Sensors+and+actuators.+A.+Physical.&rft.au=Tang%2C+Liang&rft.au=Fan%2C+Yanfeng&rft.au=Ye%2C+Fangping&rft.au=Lu%2C+Wenzheng&rft.date=2021-12-01&rft.pub=Elsevier+BV&rft.issn=0924-4247&rft.eissn=1873-3069&rft.volume=332&rft.spage=1&rft_id=info:doi/10.1016%2Fj.sna.2021.113062&rft.externalDBID=NO_FULL_TEXT
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0924-4247&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0924-4247&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0924-4247&client=summon