Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment
Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-laye...
Uloženo v:
| Vydáno v: | Sensors (Basel, Switzerland) Ročník 22; číslo 19; s. 7249 |
|---|---|
| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Basel
MDPI AG
24.09.2022
MDPI |
| Témata: | |
| ISSN: | 1424-8220, 1424-8220 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times. |
|---|---|
| AbstractList | Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times. Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the Particle Swarm Optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively; compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times. |
| Audience | Academic |
| Author | Wang, Fuchao Li, Yaobin Sun, Xin Zhang, Jingzhong Chu, Hairong Huo, Zimin Shen, Honghai |
| AuthorAffiliation | 4 Forest Protection Research Institute of Heilongjiang Province, Harbin 150040, China 3 Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China 1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China 2 University of Chinese Academy of Sciences, Beijing 100049, China |
| AuthorAffiliation_xml | – name: 2 University of Chinese Academy of Sciences, Beijing 100049, China – name: 1 Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China – name: 3 Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China – name: 4 Forest Protection Research Institute of Heilongjiang Province, Harbin 150040, China |
| Author_xml | – sequence: 1 givenname: Zimin orcidid: 0000-0003-3472-6371 surname: Huo fullname: Huo, Zimin – sequence: 2 givenname: Fuchao surname: Wang fullname: Wang, Fuchao – sequence: 3 givenname: Honghai orcidid: 0000-0002-8085-9805 surname: Shen fullname: Shen, Honghai – sequence: 4 givenname: Xin orcidid: 0000-0001-6778-3167 surname: Sun fullname: Sun, Xin – sequence: 5 givenname: Jingzhong surname: Zhang fullname: Zhang, Jingzhong – sequence: 6 givenname: Yaobin surname: Li fullname: Li, Yaobin – sequence: 7 givenname: Hairong surname: Chu fullname: Chu, Hairong |
| BookMark | eNptUstuEzEUHaEi-oAFf2CJDSym9XNmvEEKIS0VTbtoWVuO5zq48tipPYnUPR-Ok1QVrZAXtq7P4177HFcHIQaoqo8EnzIm8VmmlMiWcvmmOiKc8rqjFB_8cz6sjnO-x5gyxrp31SFrKGsYl0fVn5vV6Abt0TQOKwhZjy4GFC2az-a36OIxxWziCtB1dBnQT-0HHdC58yMk9E1n6FGBT2PY1N8nM6RDj-ZrP7q76XU9GUcIO7l57MEjF9DtWPTNjohmYeNSDEPBvK_eWu0zfHjaT6pf57O76Y_66ubicjq5qg3vxFhL3QDuuCZMEMPaRi8wUEmFYdgsODaYtMJaIjoLRgoMwkrWLRrWARDJCu2kutzr9lHfq1Uqg6dHFbVTu0JMS6VTadCDaqWWYGWPNbecYd7ZtgFNoZcLvjUuWl_3Wqv1YoDelDGS9i9EX94E91st40ZJ0TLKRBH4_CSQ4sMa8qgGlw14rwPEdVa0pYJITPHW69Mr6H1cp1Ceaosq7XWSsII63aOWugzggo3F15TVw-BMSYx1pT5peSMY6WhbCF_2BFN-OSewz90TrLbBUs_BKtizV1jjxl1Yionz_2H8Ba6rztQ |
| CitedBy_id | crossref_primary_10_1016_j_engappai_2023_107319 crossref_primary_10_3390_s23010250 crossref_primary_10_1016_j_measurement_2023_114001 crossref_primary_10_3390_machines11121079 crossref_primary_10_3390_s23052763 crossref_primary_10_3390_electronics13214278 crossref_primary_10_1109_TIM_2025_3608316 |
| Cites_doi | 10.1007/978-3-642-34396-4_40 10.3390/s19040972 10.1016/j.chaos.2022.112333 10.1364/AO.55.006243 10.1109/MAES.2004.1365016 10.1155/2018/2830686 10.3390/s20061662 10.1109/CCDC52312.2021.9601346 10.1016/j.measurement.2018.08.010 10.18653/v1/D15-1166 10.1145/3308560.3317701 10.1109/JSEN.2021.3079883 10.3788/AOS201535.0207001 10.3390/machines10060426 10.1109/ChiCC.2015.7259955 10.1109/ACCESS.2019.2912871 10.1109/ISOT.2014.23 10.20944/preprints202003.0096.v1 10.1016/0026-2714(95)00143-3 10.1109/CVPR.2015.7298965 10.1109/TIE.2012.2236994 10.1007/978-3-319-95930-6_21 10.3390/s20082203 10.3390/s20020546 10.1109/LRA.2019.2959507 10.3390/sym11010094 10.1007/s00542-015-2645-x 10.1109/ICoOM.2013.6626480 10.3390/s19081799 10.3390/s140100370 10.1109/ICRA40945.2020.9196860 10.3390/mi9050246 10.3390/s18103470 10.1145/3351180.3351220 10.1016/j.sna.2018.04.008 10.1109/NAECON.2018.8556718 10.3390/electronics8080876 10.3390/s21041518 10.1016/j.sna.2016.09.036 10.1016/j.jprocont.2020.01.004 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2022 MDPI AG 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 by the authors. 2022 |
| Copyright_xml | – notice: COPYRIGHT 2022 MDPI AG – notice: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2022 by the authors. 2022 |
| DBID | AAYXX CITATION 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s22197249 |
| DatabaseName | CrossRef ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) ProQuest Health & Medical Collection Medical Database ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | CrossRef Publicly Available Content Database MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: ProQuest - Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_79a9ef9d0a4f43048f76ea2ed9b46ab0 PMC9573235 A746531827 10_3390_s22197249 |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: Scientific Research Business Fee Fund of Heilongjiang Provincial Scientific Research Institutes grantid: CZKYF2020B009 |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c485t-9a6e084a1351c376ab0e2925c30cb40c0175ff158fec950e5f938b638ee193a13 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000867056700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Fri Oct 03 12:36:56 EDT 2025 Tue Nov 04 02:07:16 EST 2025 Sun Nov 09 14:23:22 EST 2025 Tue Oct 07 07:38:58 EDT 2025 Tue Nov 04 18:17:11 EST 2025 Sat Nov 29 07:17:47 EST 2025 Tue Nov 18 22:10:04 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 19 |
| Language | English |
| License | Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c485t-9a6e084a1351c376ab0e2925c30cb40c0175ff158fec950e5f938b638ee193a13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0001-6778-3167 0000-0002-8085-9805 0000-0003-3472-6371 |
| OpenAccessLink | https://doaj.org/article/79a9ef9d0a4f43048f76ea2ed9b46ab0 |
| PMID | 36236349 |
| PQID | 2724308913 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_79a9ef9d0a4f43048f76ea2ed9b46ab0 pubmedcentral_primary_oai_pubmedcentral_nih_gov_9573235 proquest_miscellaneous_2725190200 proquest_journals_2724308913 gale_infotracacademiconefile_A746531827 crossref_primary_10_3390_s22197249 crossref_citationtrail_10_3390_s22197249 |
| PublicationCentury | 2000 |
| PublicationDate | 20220924 |
| PublicationDateYYYYMMDD | 2022-09-24 |
| PublicationDate_xml | – month: 9 year: 2022 text: 20220924 day: 24 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationYear | 2022 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Wang (ref_42) 2016; 55 Auger (ref_37) 2013; 60 ref_14 Meng (ref_26) 2018; 130 ref_36 ref_13 ref_35 ref_12 ref_34 Zhanshe (ref_1) 2015; 21 ref_11 ref_33 ref_32 ref_31 ref_30 Bingbo (ref_9) 2015; 35 Webber (ref_19) 2021; 21 He (ref_43) 2019; 7 Narasimhappa (ref_38) 2016; 251 ref_39 ref_16 Lou (ref_28) 2022; 161 ref_15 Cao (ref_17) 2018; 2018 Awad (ref_45) 1996; 36 Fontanella (ref_18) 2018; 279 Chen (ref_29) 2020; 87 (ref_44) 2015; 26 ref_25 ref_23 ref_22 ref_20 ref_41 ref_40 ref_3 ref_2 ref_27 Brossard (ref_24) 2020; 5 ref_8 ref_5 ref_4 ref_7 Nassar (ref_10) 2004; 19 ref_6 Esfahani (ref_21) 2020; 5 |
| References_xml | – ident: ref_16 doi: 10.1007/978-3-642-34396-4_40 – volume: 5 start-page: 4796 year: 2020 ident: ref_24 article-title: Denoising IMU Gyroscopes With Deep Learning for Open-Loop Attitude Estimation publication-title: IEEE Robot. Autom. Lett. – ident: ref_27 doi: 10.3390/s19040972 – volume: 161 start-page: 2333 year: 2022 ident: ref_28 article-title: Chaotic signal denoising based on simplified convolutional denoising auto-encoder publication-title: Chaos Solitons Fractals doi: 10.1016/j.chaos.2022.112333 – volume: 55 start-page: 6243 year: 2016 ident: ref_42 article-title: Temperature drift modeling and compensation of fiber optical gyroscope based on improved support vector machine and particle swarm optimization algorithms publication-title: Appl. Opt. doi: 10.1364/AO.55.006243 – volume: 19 start-page: 32 year: 2004 ident: ref_10 article-title: Wavelet de-noising for IMU alignment publication-title: IEEE Aerosp. Electron. Syst. Mag. doi: 10.1109/MAES.2004.1365016 – volume: 2018 start-page: 2830686 year: 2018 ident: ref_17 article-title: Temperature Energy Influence Compensation for MEMS Vibration Gyroscope Based on RBF NN-GA-KF Method publication-title: Shock Vib. doi: 10.1155/2018/2830686 – ident: ref_15 doi: 10.3390/s20061662 – ident: ref_7 doi: 10.1109/CCDC52312.2021.9601346 – volume: 130 start-page: 448 year: 2018 ident: ref_26 article-title: An enhancement denoising autoencoder for rolling bearing fault diagnosis publication-title: Measurement doi: 10.1016/j.measurement.2018.08.010 – ident: ref_35 doi: 10.18653/v1/D15-1166 – ident: ref_34 doi: 10.1145/3308560.3317701 – volume: 21 start-page: 16979 year: 2021 ident: ref_19 article-title: Human Activity Recognition With Accelerometer and Gyroscope: A Data Fusion Approach publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2021.3079883 – volume: 35 start-page: 207001 year: 2015 ident: ref_9 article-title: Application of EMD Threshold Filtering for Fiber Optical Gyro Drift Signal De-Noising publication-title: Acta Opt. Sin. doi: 10.3788/AOS201535.0207001 – ident: ref_4 doi: 10.3390/machines10060426 – ident: ref_41 doi: 10.1109/ChiCC.2015.7259955 – ident: ref_14 – volume: 7 start-page: 55788 year: 2019 ident: ref_43 article-title: Particle Swarm Optimization-Based Gyro Drift Estimation Method for Inertial Navigation System publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2912871 – ident: ref_8 doi: 10.1109/ISOT.2014.23 – ident: ref_32 doi: 10.20944/preprints202003.0096.v1 – volume: 36 start-page: 457 year: 1996 ident: ref_45 article-title: Properties of the Akaike information criterion publication-title: Microelectron. Reliab. doi: 10.1016/0026-2714(95)00143-3 – ident: ref_30 doi: 10.1109/CVPR.2015.7298965 – ident: ref_31 – volume: 60 start-page: 5458 year: 2013 ident: ref_37 article-title: Industrial Applications of the Kalman Filter: A Review publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2012.2236994 – ident: ref_39 doi: 10.1007/978-3-319-95930-6_21 – ident: ref_6 doi: 10.3390/s20082203 – ident: ref_5 doi: 10.3390/s20020546 – volume: 5 start-page: 399 year: 2020 ident: ref_21 article-title: OriNet: Robust 3-D Orientation Estimation With a Single Particular IMU publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2019.2959507 – ident: ref_25 doi: 10.3390/sym11010094 – volume: 21 start-page: 2053 year: 2015 ident: ref_1 article-title: Research development of silicon MEMS gyroscopes: A review publication-title: Microsyst. Technol. doi: 10.1007/s00542-015-2645-x – volume: 26 start-page: 3046 year: 2015 ident: ref_44 article-title: Vehicle State Estimation Based on Ant Colony Optimization Algorithm publication-title: China Mech. Eng. – ident: ref_3 doi: 10.1109/ICoOM.2013.6626480 – ident: ref_13 doi: 10.3390/s19081799 – ident: ref_36 – ident: ref_12 doi: 10.3390/s140100370 – ident: ref_20 doi: 10.1109/ICRA40945.2020.9196860 – ident: ref_11 doi: 10.3390/mi9050246 – ident: ref_23 doi: 10.3390/s18103470 – ident: ref_40 doi: 10.1145/3351180.3351220 – volume: 279 start-page: 553 year: 2018 ident: ref_18 article-title: MEMS gyros temperature calibration through artificial neural networks publication-title: Sens. Actuators A Phys. doi: 10.1016/j.sna.2018.04.008 – ident: ref_22 doi: 10.1109/NAECON.2018.8556718 – ident: ref_33 doi: 10.3390/electronics8080876 – ident: ref_2 doi: 10.3390/s21041518 – volume: 251 start-page: 42 year: 2016 ident: ref_38 article-title: ARMA model based adaptive unscented fading Kalman filter for reducing drift of fiber optic gyroscope publication-title: Sens. Actuators A Phys. doi: 10.1016/j.sna.2016.09.036 – volume: 87 start-page: 54 year: 2020 ident: ref_29 article-title: One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes publication-title: J. Process. Control doi: 10.1016/j.jprocont.2020.01.004 |
| SSID | ssj0023338 |
| Score | 2.4096708 |
| Snippet | Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise... |
| SourceID | doaj pubmedcentral proquest gale crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | 7249 |
| SubjectTerms | Accuracy Algorithms Analysis Artificial intelligence attention mechanism convolutional denoising autoencoder Deep learning Kalman filter Kalman filters Mathematical optimization MEMS gyroscope Microelectromechanical systems Network topologies Neural networks Noise control Optimization algorithms Parameter estimation Particle Swarm Optimization algorithm Sensors Support vector machines temporal convolutional network Time series Wavelet transforms |
| SummonAdditionalLinks | – databaseName: Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nj9MwELWgywEOfCMCCzIICS5Rs46dxCfULS0g1FKJRVpOkePYEKmblCa7End-ODOO220BceIaTxRbM36eyXjeEPICWeQA5WxYSEeqrdNQJXEUFlwIpm2si7JvNpHO59npqVz48ujWX6vcYKID6p7tGe9tAwgPy0bjH_MhSxmPI0yxvV59D7GHFOZafUONq-QAibeyATlYvJ8tvmwDsBjisZ5dKIZQf9gyhk23kEZz50xy1P1_AvTvlyZ3TqHprf87_9vkpvdG6ag3nzvkiqnvkhs7HIX3yM-PACpnIITIATGv0yRtLJ1NZp_o2x_IhtmsDJ03VWvoB7U8UzWdVpiEp8dwRJYUxMdNfRG-GU2oqkvqan5PxvNw1HX9ZUuKHdmWtKop-r6Vdi_SyWUN3n3yeTo5Gb8LfeuGUPNMdKFUiYkyrrD_nwYMU0VkmGRCx5EueKQBB4S1RyKzRksRGWFlnBWABcaARwmvPSCDuqnNQ0LLhJe2UCphGr0PrjiPQQkQmCrJDEsC8mqjvFx7XnNsr7HMIb5BPedbPQfk-VZ01ZN5_E3oGC1gK4D82-5Bs_6a--2cp1JJY2UZKW5BkTyzaWIUM6UsOC42IC_RfnJECZiMVr7YAZaEfFv5KEVeO4jt0oAcbuwl9_DR5pfmEZBn22HY-JjNUbVpzp0MeN_g7cPH0j3T3Jv6_khdfXMU4lKkMYvFo39__DG5zrDawyXhDsmgW5-bJ-Savuiqdv3U765f6-43zQ priority: 102 providerName: ProQuest |
| Title | Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment |
| URI | https://www.proquest.com/docview/2724308913 https://www.proquest.com/docview/2725190200 https://pubmed.ncbi.nlm.nih.gov/PMC9573235 https://doaj.org/article/79a9ef9d0a4f43048f76ea2ed9b46ab0 |
| Volume | 22 |
| WOSCitedRecordID | wos000867056700001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest - Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZg4QAHxFNkd6kMQoJLtMGPJD62JQWEGipYpHKKHMcWkbrJaptdiQsnfjgzTlpaQOLCJQd7rNjjGXs-2f6GkOfIIgernAtL5Um1TRLqmEdhKaRkxnFTVn2yiSTP0-VSLXZSfeGdsJ4euFfcSaK0sk5VkRZOAPZOXRJbzWylShHr0qN1iHo2YGqAWhyQV88jxAHUn6wZw_RaSJi5s_t4kv4_l-Lfr0fu7Dezu-TOECjScd_Be-Sabe6T2zv0gQ_Ijw_g72cghE4NcNQrmbaOzrP5J_rmGxJVtueW5m29tvS9Xp3phs5qPB-nE9i9Kgri07a5Cl-PM6qbivrnuKfTPBx3XX8PkmKytBWtG4phaW18Q5r9eh73kHyeZafTt-GQVSE0IpVdqHRso1RoTM1nYHkBHVqmmDQ8MqWIDLiodO6VTJ01SkZWOsXTEtzUWgj2oNkjctC0jX1MaBWLypVax8xgYCC0EBy0BphRK2ZZHJCXG20XZqAcx8wXqwKgB05MsZ2YgDzbip73PBt_E5rglG0FkBrbF4DBFIPBFP8ymIC8wAkv0IGhM0YP7xBgSEiFVYwTpJwD2JUE5HhjE8Xg2euCQUd4hIe7AXm6rQafxIMW3dj20stAYAyBOPws2bOlva7v1zT1V8_urWTCGZeH_2OsR-QWw-ca_hTtmBx0F5f2Cblprrp6fTEi15Nl4r_piNyYZPni48i7EXzn3zMoW7ybL778BJHOJAI |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lj9MwELaWBQk48EYEFjAIBJdog-08fECo223ZVbcFia7UW3AcGyJ1k9J2F-2d38NvZMZJX4C47YFrMk7i5PPYE898HyEvkEUOvJz1M-lItXXsq4gHfibCkGnLdZbXYhPxYJCMRvLjFvm5qIXBtMqFT3SOOq80_iPfZTETPMBNtXeTbz6qRuHu6kJCo4ZFz5x_h5Bt9vZwH77vS8a6nWH7wG9UBXwtknDuSxWZIBEKpek0DC-VBYZJFmoe6EwEGiAaWvsmTKzRMgxMaCVPMoCpMbDYgWZw3UvkMvQwxhSyeLQK8DjEezV7Eecy2J0xhqJeSNO5Nuc5aYA_J4DfkzLXZrnuzf_t_dwiN5r1NG3VA-A22TLlHXJ9jWXxLvnxAdziCRih74Oo3WGRVpb2O_1P9P058nlWE0MHVTEztKfGJ6qk3QLTCOgeTPI5BfN2VZ75-60OVWVOXdXysD3wW_N5nS5KUVNuTIuS4uq90K4h7ayqCO-R4wt5DffJdlmV5gGheSRymykVMY3rJ6GE4PCZIbRWkhkWeeT1Ah6pbpjZUSBknEKEhkhKl0jyyPOl6aSmI_mb0R5ibGmADOLuQDX9kjYOKY2lksbKPFDCAnBEYuPIKGZymQnsrEdeIUJT9HPwMFo15RrQJWQMS1sxMvNBdBp7ZGeByLRxgLN0BUePPFueBteF-1GqNNWps4H4AeIVuFm8Af6NR988UxZfHQm6DGPOePjw3zd_Sq4eDPtH6dHhoPeIXGNYu-K2FHfI9nx6ah6TK_psXsymT9xIpuTzRQ-NXykdhkM |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKQQgOvBELBQwCwSXaYDtxfEBouw-oSpdKFGlvwXFsiLRNlt1tUe_8Kn4dM072BYhbD1yTyfubsSee-T5CniGLHEQ5F2TKk2obGeiYh0EmoogZx02W12ITcjhMRiN1uEV-LnphsKxyERN9oM4rg__I20wywUNcVGu7pizisDd4M_kWoIIUrrQu5DRqiOzbs--Qvs1e7_XgWz9nbNA_6r4LGoWBwIgkmgdKxzZMhEaZOgOuprPQMsUiw0OTidAAXCPnXkWJs0ZFoY2c4kkGkLUWJj5wGJz3ArkoOZcoGyFHq2SPQ-5XMxlxrsL2jDEU-ELKzrXxz8sE_DkY_F6guTbiDa7_z-_qBrnWzLNpp3aMm2TLlrfI1TX2xdvkxwcIl8dghDERsnmPUVo5etA_-EjfniHPZzWxdFgVM0v39fhYl3RQYHkB3YXBP6dg3q3K06DX6VNd5tR3Mx91h0FnPq_LSClqzY1pUVKc1RfGH0j7q-7CO-TTubyGu2S7rEp7j9A8FrnLtI6ZwXmV0EJw-OSQcmvFLItb5OUCKqlpGNtROGScQuaGqEqXqGqRp0vTSU1T8jejXcTb0gCZxf2GavolbQJVKpVW1qk81MIBiETiZGw1s7nKBD5si7xAtKYY_-BmjG7aOOCRkEks7Uhk7IOsVbbIzgKdaRMYZ-kKmi3yZLkbQhquU-nSVifeBvIKyGPgYnLDETZufXNPWXz15Ogqkpzx6P6_L_6YXAaPSN_vDfcfkCsMW1r8SuMO2Z5PT-xDcsmczovZ9JF3ako-n7dn_AIuUY73 |
| 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=Optimal+Compensation+of+MEMS+Gyroscope+Noise+Kalman+Filter+Based+on+Conv-DAE+and+MultiTCN-Attention+Model+in+Static+Base+Environment&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Zimin+Huo&rft.au=Fuchao+Wang&rft.au=Honghai+Shen&rft.au=Xin+Sun&rft.date=2022-09-24&rft.pub=MDPI+AG&rft.eissn=1424-8220&rft.volume=22&rft.issue=19&rft.spage=7249&rft_id=info:doi/10.3390%2Fs22197249&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_79a9ef9d0a4f43048f76ea2ed9b46ab0 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |