Zero-Velocity Detection—An Algorithm Evaluation

Skog, Isaac; Händel, Peter; Nilsson, John-Olof; Rantakokko, Jouni

doi:10.1109/tbme.2010.2060723

Cited by 546 publications

(412 citation statements)

References 20 publications

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“…Their detection methods are checked through the magnitude of the acceleration, magnitude of gyroscope, and local acceleration variance. Several papers, including [33] and [34], etc., have also discussed zero-velocity detection methods in detail. Readers can refer to this literature for more information on the detection procedure.…”

Section: Ins Error Correction Using Zuptmentioning

confidence: 99%

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

Lan

Zhuang

et al. 2015

Micromachines

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Numerous solutions/methods to solve the existing problems of pedestrian navigation/localization have been proposed in the last decade by both industrial and academic researchers. However, to date there are still major challenges for a single pedestrian navigation system (PNS) to operate continuously, robustly, and seamlessly in all indoor and outdoor environments. In this paper, a novel method for pedestrian navigation approach to fuse the information from two separate PNSs is proposed. When both systems are used at the same time by a specific user, a nonlinear inequality constraint between the two systems' navigation estimates always exists. Through exploring this constraint information, a novel filtering technique named Kalman filter with state constraint is used to diminish the positioning errors of both systems. The proposed method was tested by fusing the navigation information from two different PNSs, one is the foot-mounted inertial navigation system (INS) mechanization-based system, the other PNS is a navigation device that is mounted on the user's upper body, and adopting the pedestrian dead reckoning (PDR) mechanization for navigation update. Monte Carlo simulations and real field experiments show that the proposed method for the integration of multiple PNSs could improve each PNS' navigation performance. OPEN ACCESSMicromachines 2015, 6 927

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Section: Ins Error Correction Using Zuptmentioning

confidence: 99%

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

Lan

Zhuang

et al. 2015

Micromachines

View full text Add to dashboard Cite

show abstract

“…However, these detectors are concluded in an ad-hoc manner, meanwhile the literature lacks a discussion of their characteristics. In [10], the angular rate energy (ARE) detector, the acceleration magnitude (MAG) detector, the acceleration moving variance (MV) detector, and the stance hypothesis optimal (SHOE) detector, are all summarized as generalized likelihood ratio tests. The detectors provide good performance at low gait speeds.…”

Section: Introductionmentioning

confidence: 99%

A zero velocity intervals detection algorithm based on sensor fusion for indoor navigation systems

Ma¹,

Song²,

Li³

et al. 2017

Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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The Zero-velocity Update (ZUPT)-aided Extended Kalman Filter(EKF) algorithm is commonly deployed to resolve trajectories of pedestrians. To use the ZUPT, it is necessary to detect zero velocity intervals reliably. Existing zero velocity intervals detection algorithms cannot provides good performance at high gait speeds or stair climbing. In this paper, we propose a novel zero velocity intervals detection approach based on sensors fusion. In this method, the measurements of accelerometer, gyroscope and pressure sensor are combined together to detect the zero velocity intervals. A new detector is derived based on the general likelihood ratio test (LRT) framework. The test results show that the new detector performs much better than the angular rate energy (ARE) detector.

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“…These errors let the trajectories drift away from the actual path as time grows. An effective technique to bound the error growth is using Zero Velocity Update (ZUPT) [5]. The foot-mounted ZUPT-aided INS was provided as an open source in Openshoe project [6].…”

Section: Introductionmentioning

confidence: 99%

Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

Shi

Wang

2016

Preprint

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Abstract:The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems' position over the existing spherical constraint.

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Zero-Velocity Detection—An Algorithm Evaluation

Cited by 546 publications

References 20 publications

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

A Novel Kalman Filter with State Constraint Approach for the Integration of Multiple Pedestrian Navigation Systems

A zero velocity intervals detection algorithm based on sensor fusion for indoor navigation systems

Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

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