Online auto-calibration of triaxial accelerometer with time-variant model structures
In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as i...
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| Veröffentlicht in: | Sensors and actuators. A. Physical. 2017-10, Vol.266, p.294-307 |
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| container_title | Sensors and actuators. A. Physical. |
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| creator | Ye, L. Argha, A. Celler, B.G. Nguyen, H.T. Su, S.W. |
| description | In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration. |
| doi_str_mv | 10.1016/j.sna.2017.08.049 |
| format | Article |
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Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. 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A. Physical.</title><description>In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). 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Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration.</description><subject>10th century</subject><subject>Accelerometers</subject><subject>Calibration</subject><subject>Cost analysis</subject><subject>Expectation maximization algorithm</subject><subject>Linearization</subject><subject>Mathematical models</subject><subject>Microelectromechanical systems</subject><subject>Model linearization</subject><subject>Numerical analysis</subject><subject>Online calibration</subject><subject>Parameter estimation</subject><subject>Parameter identification</subject><subject>Recursive methods</subject><subject>Tri-axial accelerometer</subject><issn>0924-4247</issn><issn>1873-3069</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kD1PwzAQhi0EEqXwA9giMSf4YmM7YkIVX1KlLmW2XOciHCVxsZ0C_x6jMjPdcM97Hw8h10AroCBu-ypOpqopyIqqivLmhCxASVYyKppTsqBNzUtec3lOLmLsKaWMSbkg2800uAkLMydfWjO4XTDJ-anwXZGCM1_ODIWxFgcMfsSEofh06b1IbsTyYDIxpWL0LQ5FTGG2aQ4YL8lZZ4aIV391Sd6eHrerl3K9eX5dPaxLy6lMJUCLou5QNBwsN0LxWjXUdCCgzY_Ud7kpBZMcWbsTjWJCtbXtQAF2IJliS3JznLsP_mPGmHTv5zDllRoaKWtoKGeZgiNlg48xYKf3wY0mfGug-lee7nWWp3_laap0lpcz98cM5vMPDoOO1uFksXUBbdKtd_-kfwC9sndr</recordid><startdate>20171015</startdate><enddate>20171015</enddate><creator>Ye, L.</creator><creator>Argha, A.</creator><creator>Celler, B.G.</creator><creator>Nguyen, H.T.</creator><creator>Su, S.W.</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>7U5</scope><scope>8FD</scope><scope>FR3</scope><scope>L7M</scope></search><sort><creationdate>20171015</creationdate><title>Online auto-calibration of triaxial accelerometer with time-variant model structures</title><author>Ye, L. ; Argha, A. ; Celler, B.G. ; Nguyen, H.T. ; Su, S.W.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c407t-11de62fe6941c4a6842890af161d0172562f76374e3db698368d2cf181ef17383</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>10th century</topic><topic>Accelerometers</topic><topic>Calibration</topic><topic>Cost analysis</topic><topic>Expectation maximization algorithm</topic><topic>Linearization</topic><topic>Mathematical models</topic><topic>Microelectromechanical systems</topic><topic>Model linearization</topic><topic>Numerical analysis</topic><topic>Online calibration</topic><topic>Parameter estimation</topic><topic>Parameter identification</topic><topic>Recursive methods</topic><topic>Tri-axial accelerometer</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ye, L.</creatorcontrib><creatorcontrib>Argha, A.</creatorcontrib><creatorcontrib>Celler, B.G.</creatorcontrib><creatorcontrib>Nguyen, H.T.</creatorcontrib><creatorcontrib>Su, S.W.</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Sensors and actuators. A. Physical.</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ye, L.</au><au>Argha, A.</au><au>Celler, B.G.</au><au>Nguyen, H.T.</au><au>Su, S.W.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Online auto-calibration of triaxial accelerometer with time-variant model structures</atitle><jtitle>Sensors and actuators. A. Physical.</jtitle><date>2017-10-15</date><risdate>2017</risdate><volume>266</volume><spage>294</spage><epage>307</epage><pages>294-307</pages><issn>0924-4247</issn><eissn>1873-3069</eissn><abstract>In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. 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| source | Elsevier ScienceDirect Journals |
| subjects | 10th century Accelerometers Calibration Cost analysis Expectation maximization algorithm Linearization Mathematical models Microelectromechanical systems Model linearization Numerical analysis Online calibration Parameter estimation Parameter identification Recursive methods Tri-axial accelerometer |
| title | Online auto-calibration of triaxial accelerometer with time-variant model structures |
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