Inertial sensor array processing with motion models
Wahlstrom, J; Skog, I; Handel, P
Date: 6 September 2018
Publisher
IEEE
Publisher DOI
Abstract
By arranging a large number of inertial sensors in
an array and fusing their measurements, it is possible to create
inertial sensor assemblies with a high performance-to-price ratio.
Recently, a maximum likelihood estimator for fusing inertial
array measurements collected at a given sampling instance was
developed. In this paper, ...
By arranging a large number of inertial sensors in
an array and fusing their measurements, it is possible to create
inertial sensor assemblies with a high performance-to-price ratio.
Recently, a maximum likelihood estimator for fusing inertial
array measurements collected at a given sampling instance was
developed. In this paper, the maximum likelihood estimator
is extended by introducing a motion model and deriving a
maximum a posteriori estimator that jointly estimates the array
dynamics at multiple sampling instances. Simulation examples
are used to demonstrate that the proposed sensor fusion method
have the potential to yield significant improvements in estimation
accuracy. Further, by including the motion model, we resolve the
sign ambiguity of gyro-free implementations, and thereby open
up for implementations based on accelerometer-only arrays.
Computer Science
Faculty of Environment, Science and Economy
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