Determination of stochastic model parameters of inertial sensors

Download

index.pdf

Date

2013

Author

Ünver, Alper

Metadata

Show full item record

Item Usage Stats

224
views

102
downloads

Gyro and accelerometer systematic errors due to biases, scale factors, and misalignments can be compensated via an on-board Kalman filtering approach in a Navigation System. On the other hand, sensor random noise sources such as Quantization Noise (QN), Angular Random Walk (ARW), Flicker Noise (FN), and Rate Random Walk (RRW) are not easily estimated by an on-board filter, due to their random characteristics. In this thesis a new method based on the variance of difference sequences is proposed to compute the powers of the above mentioned noise sources. The method is capable of online or offline estimation of stochastic model parameters of the inertial sensors. Our aim in this study is the estimation of ARW, FN and RRW parameters besides the quantization and the Gauss-Markov noise parameters of the inertial sensors. The proposed method is tested both on the simulated and the real sensor data and the results are compared with the Allan variance method. Comparison shows very satisfactory results for the performance of the method. Computational load of the new method is less than the computational load of the Allan variance on the order of tens. One of the usages of this method is the individual noise characterization. A noise, whose power spectral density has a constant slope, can be identified accurately by the proposed method. In addition to this, the parameters of the GM noise can also be determined. Another idea developed here is to approximate the overall error source as a combination of ARW and some number of GM sources only. The reasons of selecting such a structure is the feasibility of using these models in a Kalman filter framework for error propagation as well as their generality of modeling other noise sources.

Subject Keywords

Inertial sensors., Noise generators (Electronics)., Stochastic models., Stochastic processes., Random noise theory.

URI

http://etd.lib.metu.edu.tr/upload/12615548/index.pdf
https://hdl.handle.net/11511/22259

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Identification of inertial sensor error parameters Altınöz, Bağış; Leblebicioğlu, Mehmet Kemal; Department of Electrical and Electronics Engineering (2015) Inertial sensors (gyroscopes and accelerometers) that are used in navigation systems have distinct error characteristics such as bias, scale factor, random walk, etc. Calibration and characterization tests are done with 2 or 3 axes rate tables in order to identify these errors. It is possible to utilize error characteristics of these devices, and the navigation accuracy is directly dependent on the accuracy of this identification process. In this thesis, inertial sensor error parameters are identified by a ...
Simulation of Dynamic Operation in Salient Pole Synchronous Machines Dordea, Toma; Munteanu, Radu; Campeanu, Aurel (2011-09-10) The design of today's high performance synchronous motors must take into account not only the steady state but also practically inevitable dynamic regimes. In such conditions an accurate predetermination of the parameters and constructive solution by modeling and simulation of the given dynamic operation, becomes a mandatory step. In this aim, in the present paper a novel mathematical model of the high power salient pole synchronous machine (SPSM) is elaborated. We proposed to consider especially the effect...
Discrete sizing optimization of steel trusses under multiple displacement constraints and load cases using guided stochastic search technique Azad, S. Kazemzadeh; Hasançebi, Oğuzhan (2015-08-01) The guided stochastic search (GSS) is a computationally efficient design optimization technique, which is originally developed for discrete sizing optimization problems of steel trusses with a single displacement constraint under a single load case. The present study aims to investigate the GSS in a more general class of truss sizing optimization problems subject to multiple displacement constraints and load cases. To this end, enhancements of the GSS are proposed in the form of two alternative approaches t...
Estimation of the Hurst parameter for fractional Brownian motion using the CMARS method Yerlikaya-Ozkurt, F.; Vardar Acar, Ceren; Yolcu-Okur, Y.; Weber, G. -W. (2014-03-15) In this study, we develop an alternative method for estimating the Hurst parameter using the conic multivariate adaptive regression splines (CMARS) method. We concentrate on the strong solutions of stochastic differential equations (SDEs) driven by fractional Brownian motion (fBm). Our approach is superior to others in that it not only estimates the Hurst parameter but also finds spline parameters of the stochastic process in an adaptive way. We examine the performance of our estimations using simulated tes...
Assessment of alternative simulation techniques in nonlinear time history analyses of multi-story frame buildings: A case study Karim Zadeh Naghshineh, Shaghayegh; Askan Gündoğan, Ayşegül; Yakut, Ahmet (2017-07-01) In regions with sparse ground motion data, simulations provide alternative acceleration time series for evaluation of the dynamic response of a structure. Different ground motion simulation methods provide varying levels of goodness of fit between observed and synthetic data. Before using the seismologically acceptable synthetic records for engineering purposes, it is critical to investigate the efficiency of synthetics in predicting observed seismic responses of structures. For this purpose, in this study ...

Citation Formats

A. Ünver, “Determination of stochastic model parameters of inertial sensors,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.