Automatic Determination of Validity of Input Data Used in Ellipsoid Fitting MARG Calibration Algorithms

Sensors

Alberto Olivares¹  Gonzalo Ruiz-Garcia²  Gonzalo Olivares²  Juan Manuel Górriz¹ 

[1] Department of Signal Theory, Telematics and Communications, CITIC-University of Granada, Calle Periodista Rafael Gómez Montero, 2, E-18071 Granada, Spain; E-Mails:;Department of Computer Architecture and Computer Technology, CITIC-University of Granada, Calle Periodista Rafael Gómez Montero, 2, E-18071 Granada, Spain; E-Mails:

关键词: calibration;    accelerometer;    magnetometer;    MARG;    MEMS;    automatic;    validation;    fuzzy;    FLS;    thresholding;   

DOI  :  10.3390/s130911797

来源: mdpi

【 摘 要 】

Ellipsoid fitting algorithms are widely used to calibrate Magnetic Angular Rate and Gravity (MARG) sensors. These algorithms are based on the minimization of an error function that optimizes the parameters of a mathematical sensor model that is subsequently applied to calibrate the raw data. The convergence of this kind of algorithms to a correct solution is very sensitive to input data. Input calibration datasets must be properly distributed in space so data can be accurately fitted to the theoretical ellipsoid model. Gathering a well distributed set is not an easy task as it is difficult for the operator carrying out the maneuvers to keep a visual record of all the positions that have already been covered, as well as the remaining ones. It would be then desirable to have a system that gives feedback to the operator when the dataset is ready, or to enable the calibration process in auto-calibrated systems. In this work, we propose two different algorithms that analyze the goodness of the distributions by computing four different indicators. The first approach is based on a thresholding algorithm that uses only one indicator as its input and the second one is based on a Fuzzy Logic System (FLS) that estimates the calibration error for a given calibration set using a weighted combination of two indicators. Very accurate classification between valid and invalid datasets is achieved with average Area Under Curve (AUC) of up to 0.98.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.