Journal ArticleDOI
Integration of MEMS Inertial and Pressure Sensors for Vertical Trajectory Determination
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TLDR
This paper investigates the integration of a MEMS barometric pressure sensor with the MEMS-IMU for vertical position/velocity tracking without the GPS that has applications in sports and proposes a cascaded two-step Kalman filter.Abstract:
Integration of a low-cost global positioning system (GPS) with a microelectromechanical system-based inertial measurement unit (MEMS-IMU) is a widely used method that takes advantage of the individual superiority of each system to get a more accurate and robust navigation performance. However, because of poor observations as well as multipath effects, the GPS has low accuracy in the vertical direction. As a result, the navigation accuracy even in an integrated GPS/MEMS-IMU system is more challenged in the vertical direction than the horizontal direction. To overcome this problem, this paper investigates the integration of a MEMS barometric pressure sensor with the MEMS-IMU for vertical position/velocity tracking without the GPS that has applications in sports. A cascaded two-step Kalman filter consisting of separate orientation and position/velocity subsystems is proposed for this integration. Slow human movements in addition to more rapid sport activities such as vertical and step-down jumps can be tracked using the proposed algorithm. The height-tracking performance is benchmarked against a reference camera-based motion-tracking system and an error analysis is performed. The experimental results show that the vertical trajectory tracking error is less than 28.1 cm. For the determination of jump vertical height/drop, the proposed algorithm has an error of less than 5.8 cm.read more
Citations
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Journal ArticleDOI
Motion Mode Recognition for Indoor Pedestrian Navigation Using Portable Devices
TL;DR: The performance of the motion mode recognition module was examined on different types of mobile computing devices, including various brands of smartphones, tablets, smartwatches, and smartglasses, and the results obtained showed the capability of enhancing positioning performance.
Journal ArticleDOI
A Novel Biomechanical Model-Aided IMU/UWB Fusion for Magnetometer-Free Lower Body Motion Capture
TL;DR: A magnetometer-free algorithm for lower-body MoCap including 3-D localization and posture tracking by fusing inertial sensors with an ultrawideband (UWB) localization system and a biomechanical model of the human lower body is introduced.
Journal ArticleDOI
A cascaded Kalman filter-based GPS/MEMS-IMU integration for sports applications
TL;DR: In this article, a cascaded linear Kalman filter was proposed for trajectory determination in sports applications, which avoids the need to propagate additional states, resulting in the covariance propagation to become more computationally efficient.
Journal ArticleDOI
UWB-Aided Inertial Motion Capture for Lower Body 3-D Dynamic Activity and Trajectory Tracking
TL;DR: The experimental results show that the proposed fusion method can accurately capture the dynamic activities of a subject without drift and can maintain similar accuracies between fast and slow motions in lower body MoCap and 3-D trajectory tracking.
Journal ArticleDOI
Regression Model-Based Walking Speed Estimation Using Wrist-Worn Inertial Sensor.
TL;DR: Using the arm swing motion in walking, this paper proposes a regression model-based method for longitudinal walking speed estimation using a wrist-worn IMU and proposes a novel kinematic variable, called pca-acc, which finds the wrist acceleration in the principal axis.
References
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Book
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
TL;DR: With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory.
Journal ArticleDOI
Sigma-point Kalman filtering for integrated GPS and inertial navigation
TL;DR: Simulation and experimental results are shown to compare the performance of the sigma-point filter with a standard EKF approach, which shows faster convergence from inaccurate initial conditions in position/attitude estimation problems.
Proceedings ArticleDOI
Real-Time Attitude and Position Estimation for Small UAVs Using Low-Cost Sensors
Derek Kingston,Randal W. Beard +1 more
TL;DR: A novel method for dealing with latency in real-time is presented using a distributed-in-time architecture and a cascaded filter approach to position estimation in connection with an Extended Kalman Filter attitude estimation scheme.
Journal ArticleDOI
Study on Innovation Adaptive EKF for In-Flight Alignment of Airborne POS
Fang Jiancheng,Yang Sheng +1 more
TL;DR: In this paper, an adaptive filtering algorithm of an extended Kalman filter (EKF) combined with innovation-based adaptive estimation is proposed, which introduces the calculated innovation covariance into the computation of the filter gain matrix directly.
Journal ArticleDOI
Estimation of Attitude and External Acceleration Using Inertial Sensor Measurement During Various Dynamic Conditions
TL;DR: Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy; two different approaches for dealing with the estimation problem during dynamic conditions were compared.