期刊
IEEE SENSORS LETTERS
卷 3, 期 6, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSENS.2019.2917055
关键词
Sensor signals processing; sensor applications; adaptive thresholding; foot-mounted inertial navigation; indoor localization; posterior odds ratio; zero-velocity updates
资金
- National Institute of Standards and Technology via the grant Pervasive, Accurate, and Reliable Location-Based Services for Emergency Responders [70NANB17H185]
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.
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