Modeling inertial sensors errors using Allan variance

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dc.contributor.advisorEl-Sheimy, Naser
dc.contributor.authorHou, Haiying
dc.date.accessioned2005-08-16T17:03:12Z
dc.date.available2005-08-16T17:03:12Z
dc.date.issued2004
dc.descriptionBibliography: p. 123-128en
dc.description.abstractIt is well known that Inertial Navigation Systems (INS) can provide high accuracy information on the position, velocity, and attitude over a short time period. However, their accuracy degrades rapidly with time. The requirements for accurate estimation of navigation information necessitate the modeling of the sensors' noise components. Several methods have been devised for stochastic modeling of inertial sensors noise. Each of them is useful but each has its own disadvantage. The Adaptive Kalman filter is one of the mostly used methods to estimate inertial sensor noise, but the form of the model needs to be known first. Frequency domain approaching uses the power spectral density to estimate transfer functions. It is straightforward but it is difficult for non­system analysts to understand. In the time domain methods, the correlation function approach is very model sensitive. Variance techniques are basically very similar, and primarily differ only in that various signal processing, by way of weighting functions, window functions, etc., are incorporated into the analysis algorithms in order to achieve a particular desired result for improving the model characterizations. The simplest technique is the Allan variance method. Allan variance is a method of representing root mean square (RMS) random drift error as a function of average time. It is simple to compute and relatively simple to interpret and understand. Allan variance method can be used to determine the character of the underlying random processes that give rise to the data noise. This technique can be used to characterize various types of noise terms in the inertial sensor data by performing certain operations on the entire length of data. In this thesis, the Allan variance technique is used in noise analysis of different grade Inertial Measurement Units (IMU), which include: • Navigation grade IMU: The Honeywell Commercial IMU (CIMU); • Tactical grade IMU: The Honeywell HG 1700; and ■ Consumer grade MEMS based IMU: The Systron Donner MotionPak II-3g By performing a simple operation on the entire length of data, a characteristic curve is obtained whose inspection provides a systematic characterization of various random errors contained in the inertial sensor output data. Being a directly measurable quantity, Allan variance can provide information on the types and magnitude of various noise terms. The research work will cover both the theoretical basis for Allan Variance for modeling inertial sensors noise terms, and its implementation in modeling different noise terms existing in the different grade inertial sensors. Simple implementation and ease of interpretation make the Allan variance method suitable in inertial sensor noise identification and stochastic modeling.en
dc.format.extentxix, 128 leaves : ill. ; 30 cm.en
dc.identifier.citationHou, H. (2004). Modeling inertial sensors errors using Allan variance (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/21729en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/21729
dc.identifier.isbn0612975339en
dc.identifier.lccAC1 .T484 2004 H68en
dc.identifier.urihttp://hdl.handle.net/1880/41609
dc.language.isoeng
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.titleModeling inertial sensors errors using Allan variance
dc.typemaster thesis
thesis.degree.disciplineGeomatics Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameMaster of Science (MSc)
ucalgary.item.requestcopytrue
ucalgary.thesis.accessionTheses Collection 58.002:Box 1508 520492025
ucalgary.thesis.notesUARCen
ucalgary.thesis.uarcreleaseyen
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