SENSOR CALIBRATION USING NONPARAMETRIC STATISTICAL CHARACTERIZATION OF ERROR MODELS
SENSOR CALIBRATION USING NONPARAMETRIC STATISTICAL CHARACTERIZATION OF ERROR MODELS
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Date
2004-10
Authors
Feng, J.
Qu, G.
Potkonjak, M.
Advisor
Citation
J. Feng, G. Qu, and M. Potkonjak. "Sensor Calibration using Nonparametric Statistical Characterization of Error Models," 3rd IEEE Conference on Sensors (Sensors'2004), pp. 1456-1459, October 2004.
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Abstract
Calibration is the process of identifying and correcting for the
systematic bias component of the error in sensor measurements.
Traditionally, calibration has usually been conducted by
considering a set of measurements in a single time frame and
restricted to linear systems with the assumption of equal-quality
sensors and single modality.
The basis for the new calibration procedure is to construct a
statistical error model that captures the characteristics of the
measurement errors. Such an error model can be constructed
either off-line or on-line. It is derived using the nonparametric
kernel density estimation techniques. We propose four
alternatives to make the transition from the constructed error
model to the calibration model, which is represented by piecewise
polynomials. In addition, statistical validation and
evaluation methods such as resubstitution, is used in order to
establish the interval of confidence for both the error model and
the calibration model. Traces of the distance ranging
measurements recorded by in-field deployed sensors are used as
our demonstrative example.