Zero-velocity detection—a Bayesian approach to adaptive thresholding
Wahlstrom, J; Skog, I; Gustafsson, F; et al.Markham, A; Trigoni, N
Date: 15 May 2019
Journal
IEEE Sensors Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector
extends existing zero-velocity detectors based on the likelihood-ratio test and allows, possibly time-dependent, prior
information about the two hypotheses—the sensors being stationary or in motion—to be incorporated into the ...
A Bayesian zero-velocity detector for foot-mounted inertial navigation systems is presented. The detector
extends existing zero-velocity detectors based on the likelihood-ratio test and allows, possibly time-dependent, prior
information about the two hypotheses—the sensors being stationary or in motion—to be incorporated into the test. It is
also possible to incorporate information about the cost of a missed detection or a false alarm. Specifically, we consider
a hypothesis prior based on the velocity estimates provided by the navigation system and an exponential model for how
the cost of a missed detection increases with the time since the last zero-velocity update. Thereby, we obtain a detection
threshold that adapts to the motion characteristics of the user. Thus, the proposed detection framework efficiently solves
one of the key challenges in current zero-velocity-aided inertial navigation systems: the tuning of the zero-velocity detection
threshold. A performance evaluation on data with normal and fast gait demonstrates that the proposed detection framework
outperforms any detector that chooses two separate fixed thresholds for the two gait speeds.
Computer Science
Faculty of Environment, Science and Economy
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