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arXiv · 1903.07929

Zero-Velocity Detection - A Bayesian Approach to Adaptive Thresholding

Johan Wahlström·Isaac Skog·Fredrik Gustafsson·Andrew Markham·Niki Trigoni
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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 test. It is also possible to incorporate information about the cost of a missed detection or a false alarm. Specifically, we consider an 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.

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Johan Wahlström, Isaac Skog, Fredrik Gustafsson, Andrew Markham, Niki Trigoni. 2019-03-19. Zero-Velocity Detection - A Bayesian Approach to Adaptive Thresholding. https://arxiv.org/abs/1903.07929

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