7 research outputs found
Zero-Velocity Detection - A Bayesian Approach to Adaptive Thresholding
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
Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification
We present a method to improve the accuracy of a foot-mounted,
zero-velocity-aided inertial navigation system (INS) by varying estimator
parameters based on a real-time classification of motion type. We train a
support vector machine (SVM) classifier using inertial data recorded by a
single foot-mounted sensor to differentiate between six motion types (walking,
jogging, running, sprinting, crouch-walking, and ladder-climbing) and report
mean test classification accuracy of over 90% on a dataset with five different
subjects. From these motion types, we select two of the most common (walking
and running), and describe a method to compute optimal zero-velocity detection
parameters tailored to both a specific user and motion type by maximizing the
detector F-score. By combining the motion classifier with a set of optimal
detection parameters, we show how we can reduce INS position error during mixed
walking and running motion. We evaluate our adaptive system on a total of 5.9
km of indoor pedestrian navigation performed by five different subjects moving
along a 130 m path with surveyed ground truth markers.Comment: In Proceedings of the International Conference on Indoor Positioning
and Indoor Navigation (IPIN'17), Sapporo, Japan, Sep. 18-21, 201
A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors
Zero velocity update (ZUPT) plays an important role in pedestrian navigation algorithms with the premise that the zero velocity interval (ZVI) should be detected accurately and effectively. A novel adaptive ZVI detection algorithm based on a smoothed pseudo Wigner–Ville distribution to remove multiple frequencies intelligently (SPWVD-RMFI) is proposed in this paper. The novel algorithm adopts the SPWVD-RMFI method to extract the pedestrian gait frequency and to calculate the optimal ZVI detection threshold in real time by establishing the function relationships between the thresholds and the gait frequency; then, the adaptive adjustment of thresholds with gait frequency is realized and improves the ZVI detection precision. To put it into practice, a ZVI detection experiment is carried out; the result shows that compared with the traditional fixed threshold ZVI detection method, the adaptive ZVI detection algorithm can effectively reduce the false and missed detection rate of ZVI; this indicates that the novel algorithm has high detection precision and good robustness. Furthermore, pedestrian trajectory positioning experiments at different walking speeds are carried out to evaluate the influence of the novel algorithm on positioning precision. The results show that the ZVI detected by the adaptive ZVI detection algorithm for pedestrian trajectory calculation can achieve better performance