On the Design of Attitude-Heading Reference Systems Using the Allan Variance
The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hidalgo-Carrio, Javier [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on ultrasonics, ferroelectrics, and frequency control - New York, NY : IEEE, 1986, 63(2016), 4, Seite 656-665 |
|---|---|
| Übergeordnetes Werk: |
volume:63 ; year:2016 ; number:4 ; pages:656-665 |
| Links: |
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| DOI / URN: |
10.1109/TUFFC.2016.2519268 |
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OLC1973548003 |
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| 520 | |a The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). | ||
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10.1109/TUFFC.2016.2519268 doi PQ20160430 (DE-627)OLC1973548003 (DE-599)GBVOLC1973548003 (PRQ)c955-e121bff15e60bc9f56f70a287a8485a9ccea6b04e4687bea59a1c2a8eb477600 (KEY)0013324820160000063000400656onthedesignofattitudeheadingreferencesystemsusingt DE-627 ger DE-627 rakwb eng 520 620 530 DNB Hidalgo-Carrio, Javier verfasserin aut On the Design of Attitude-Heading Reference Systems Using the Allan Variance 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). Sensor phenomena and characterization Robot sensing systems Stochastic processes error modelling Sensor fusion Allan variance inertial navigation systems Navigation sensor characterization Arnold, Sascha oth Poulakis, Pantelis oth Enthalten in IEEE transactions on ultrasonics, ferroelectrics, and frequency control New York, NY : IEEE, 1986 63(2016), 4, Seite 656-665 (DE-627)129191442 (DE-600)53308-7 (DE-576)014456540 0885-3010 nnns volume:63 year:2016 number:4 pages:656-665 http://dx.doi.org/10.1109/TUFFC.2016.2519268 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7386705 http://www.ncbi.nlm.nih.gov/pubmed/26800535 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_95 AR 63 2016 4 656-665 |
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10.1109/TUFFC.2016.2519268 doi PQ20160430 (DE-627)OLC1973548003 (DE-599)GBVOLC1973548003 (PRQ)c955-e121bff15e60bc9f56f70a287a8485a9ccea6b04e4687bea59a1c2a8eb477600 (KEY)0013324820160000063000400656onthedesignofattitudeheadingreferencesystemsusingt DE-627 ger DE-627 rakwb eng 520 620 530 DNB Hidalgo-Carrio, Javier verfasserin aut On the Design of Attitude-Heading Reference Systems Using the Allan Variance 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). Sensor phenomena and characterization Robot sensing systems Stochastic processes error modelling Sensor fusion Allan variance inertial navigation systems Navigation sensor characterization Arnold, Sascha oth Poulakis, Pantelis oth Enthalten in IEEE transactions on ultrasonics, ferroelectrics, and frequency control New York, NY : IEEE, 1986 63(2016), 4, Seite 656-665 (DE-627)129191442 (DE-600)53308-7 (DE-576)014456540 0885-3010 nnns volume:63 year:2016 number:4 pages:656-665 http://dx.doi.org/10.1109/TUFFC.2016.2519268 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7386705 http://www.ncbi.nlm.nih.gov/pubmed/26800535 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_95 AR 63 2016 4 656-665 |
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10.1109/TUFFC.2016.2519268 doi PQ20160430 (DE-627)OLC1973548003 (DE-599)GBVOLC1973548003 (PRQ)c955-e121bff15e60bc9f56f70a287a8485a9ccea6b04e4687bea59a1c2a8eb477600 (KEY)0013324820160000063000400656onthedesignofattitudeheadingreferencesystemsusingt DE-627 ger DE-627 rakwb eng 520 620 530 DNB Hidalgo-Carrio, Javier verfasserin aut On the Design of Attitude-Heading Reference Systems Using the Allan Variance 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). Sensor phenomena and characterization Robot sensing systems Stochastic processes error modelling Sensor fusion Allan variance inertial navigation systems Navigation sensor characterization Arnold, Sascha oth Poulakis, Pantelis oth Enthalten in IEEE transactions on ultrasonics, ferroelectrics, and frequency control New York, NY : IEEE, 1986 63(2016), 4, Seite 656-665 (DE-627)129191442 (DE-600)53308-7 (DE-576)014456540 0885-3010 nnns volume:63 year:2016 number:4 pages:656-665 http://dx.doi.org/10.1109/TUFFC.2016.2519268 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7386705 http://www.ncbi.nlm.nih.gov/pubmed/26800535 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_95 AR 63 2016 4 656-665 |
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10.1109/TUFFC.2016.2519268 doi PQ20160430 (DE-627)OLC1973548003 (DE-599)GBVOLC1973548003 (PRQ)c955-e121bff15e60bc9f56f70a287a8485a9ccea6b04e4687bea59a1c2a8eb477600 (KEY)0013324820160000063000400656onthedesignofattitudeheadingreferencesystemsusingt DE-627 ger DE-627 rakwb eng 520 620 530 DNB Hidalgo-Carrio, Javier verfasserin aut On the Design of Attitude-Heading Reference Systems Using the Allan Variance 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). Sensor phenomena and characterization Robot sensing systems Stochastic processes error modelling Sensor fusion Allan variance inertial navigation systems Navigation sensor characterization Arnold, Sascha oth Poulakis, Pantelis oth Enthalten in IEEE transactions on ultrasonics, ferroelectrics, and frequency control New York, NY : IEEE, 1986 63(2016), 4, Seite 656-665 (DE-627)129191442 (DE-600)53308-7 (DE-576)014456540 0885-3010 nnns volume:63 year:2016 number:4 pages:656-665 http://dx.doi.org/10.1109/TUFFC.2016.2519268 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7386705 http://www.ncbi.nlm.nih.gov/pubmed/26800535 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_95 AR 63 2016 4 656-665 |
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10.1109/TUFFC.2016.2519268 doi PQ20160430 (DE-627)OLC1973548003 (DE-599)GBVOLC1973548003 (PRQ)c955-e121bff15e60bc9f56f70a287a8485a9ccea6b04e4687bea59a1c2a8eb477600 (KEY)0013324820160000063000400656onthedesignofattitudeheadingreferencesystemsusingt DE-627 ger DE-627 rakwb eng 520 620 530 DNB Hidalgo-Carrio, Javier verfasserin aut On the Design of Attitude-Heading Reference Systems Using the Allan Variance 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). Sensor phenomena and characterization Robot sensing systems Stochastic processes error modelling Sensor fusion Allan variance inertial navigation systems Navigation sensor characterization Arnold, Sascha oth Poulakis, Pantelis oth Enthalten in IEEE transactions on ultrasonics, ferroelectrics, and frequency control New York, NY : IEEE, 1986 63(2016), 4, Seite 656-665 (DE-627)129191442 (DE-600)53308-7 (DE-576)014456540 0885-3010 nnns volume:63 year:2016 number:4 pages:656-665 http://dx.doi.org/10.1109/TUFFC.2016.2519268 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7386705 http://www.ncbi.nlm.nih.gov/pubmed/26800535 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY GBV_ILN_70 GBV_ILN_95 AR 63 2016 4 656-665 |
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520 620 530 DNB On the Design of Attitude-Heading Reference Systems Using the Allan Variance Sensor phenomena and characterization Robot sensing systems Stochastic processes error modelling Sensor fusion Allan variance inertial navigation systems Navigation sensor characterization |
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ddc 520 misc Sensor phenomena and characterization misc Robot sensing systems misc Stochastic processes misc error modelling misc Sensor fusion misc Allan variance misc inertial navigation systems misc Navigation misc sensor characterization |
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ddc 520 misc Sensor phenomena and characterization misc Robot sensing systems misc Stochastic processes misc error modelling misc Sensor fusion misc Allan variance misc inertial navigation systems misc Navigation misc sensor characterization |
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ddc 520 misc Sensor phenomena and characterization misc Robot sensing systems misc Stochastic processes misc error modelling misc Sensor fusion misc Allan variance misc inertial navigation systems misc Navigation misc sensor characterization |
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IEEE transactions on ultrasonics, ferroelectrics, and frequency control |
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IEEE transactions on ultrasonics, ferroelectrics, and frequency control |
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On the Design of Attitude-Heading Reference Systems Using the Allan Variance |
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On the Design of Attitude-Heading Reference Systems Using the Allan Variance |
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Hidalgo-Carrio, Javier |
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IEEE transactions on ultrasonics, ferroelectrics, and frequency control |
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Hidalgo-Carrio, Javier |
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10.1109/TUFFC.2016.2519268 |
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on the design of attitude-heading reference systems using the allan variance |
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On the Design of Attitude-Heading Reference Systems Using the Allan Variance |
| abstract |
The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). |
| abstractGer |
The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). |
| abstract_unstemmed |
The Allan variance is a method to characterize stochastic random processes. The technique was originally developed to characterize the stability of atomic clocks and has also been successfully applied to the characterization of inertial sensors. Inertial navigation systems (INS) can provide accurate results in a short time, which tend to rapidly degrade in longer time intervals. During the last decade, the performance of inertial sensors has significantly improved, particularly in terms of signal stability, mechanical robustness, and power consumption. The mass and volume of inertial sensors have also been significantly reduced, offering system-level design and accommodation advantages. This paper presents a complete methodology for the characterization and modeling of inertial sensors using the Allan variance, with direct application to navigation systems. Although the concept of sensor fusion is relatively straightforward, accurate characterization and sensor-information filtering is not a trivial task, yet they are essential for good performance. A complete and reproducible methodology utilizing the Allan variance, including all the intermediate steps, is described. An end-to-end (E2E) process for sensor-error characterization and modeling up to the final integration in the sensor-fusion scheme is explained in detail. The strength of this approach is demonstrated with representative tests on novel, high-grade inertial sensors. Experimental navigation results are presented from two distinct robotic applications: a planetary exploration rover prototype and an autonomous underwater vehicle (AUV). |
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On the Design of Attitude-Heading Reference Systems Using the Allan Variance |
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http://dx.doi.org/10.1109/TUFFC.2016.2519268 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7386705 http://www.ncbi.nlm.nih.gov/pubmed/26800535 |
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Arnold, Sascha Poulakis, Pantelis |
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