Loading...
Search for:
position-and-attitude-estimation
0.005 seconds
Integrated Navigation for Attitude and Orbit Estimation of Nano satellite with Online Calibration
, M.Sc. Thesis Sharif University of Technology ; Salarieh, Hassan (Supervisor) ; Alasti, Aria (Supervisor) ; Jalili, Hadi (Co-Supervisor)
Abstract
In this study, integrated navigation for attitude and orbit estimation of nanosatellite with online magnetometer calibration is derived. This algorithm has been developed based on the Extended Kalman Filter and Unscented Kalman Filter and the unknown magnetometer’s parameters (such as bias vector and scale factor orthogonal matrix and non-orthognal misallignmet matrix) are estimated in order to calibration of this sensor. In order to determine the location and orientation of the satellite, many different algorithms have been developed which have the ability to determine the location without the need for GPS sensor; it is necessary to be calibrated and ensure proper performance of these...
Inertial motion capture accuracy improvement by kalman smoothing and dynamic networks
, Article IEEE Sensors Journal ; Volume 21, Issue 3 , 2021 , Pages 3722-3729 ; 1530437X (ISSN) ; Salarieh, H ; Alasty, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2021
Abstract
Localization-capable inertial motion capture algorithms rely on zero-velocity updates (ZUPT), usually as measurements in a Kalman filtering scheme, for position and attitude error control. As ZUPTs are only applicable during the static phases a link goes through, estimation errors grow during dynamic ones. This error growth may somewhat be mitigated by imposing biomechanical constraints in multi-sensor systems. Error reduction is also possible by optimization-based methods that incorporate the dynamic and static constraints governing the system behavior over a period of time (e.g. the dynamic network algorithm); when this period includes multiple static phases for a link, its estimation...
Augmenting Inertial Motion Capture with SLAM Using EKF and SRUKF Data Fusion Algorithms
, M.Sc. Thesis Sharif University of Technology ; Salarieh, Hassan (Supervisor)
Abstract
Inertial motion capture systems widely use low-cost IMUs to obtain the orientation of human body segments, but these sensors alone are unable to estimate link positions. Therefore, this research used a SLAM method in conjunction with inertial data fusion to estimate link positions. SLAM is a method that tracks a target in a reconstructed map of the environment using a camera. This paper proposes quaternion-based extended and square-root unscented Kalman filters (EKF & SRUKF) algorithms for pose estimation. The Kalman filters use measurements based on SLAM position data, multi-link biomechanical constraints, and vertical referencing to correct errors. In addition to the sensor biases, the...
Towards real-time partially self-calibrating pedestrian navigation with an inertial sensor array
, Article IEEE Sensors Journal ; Volume 20, Issue 12 , 2020 , Pages 6634-6641 ; Salarieh, H ; Alasty, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Inspired by algorithms utilized in inertial navigation, an inertial motion capturing algorithm capable of position and heading estimation is introduced. The fusion algorithm is capable of real-time link geometry estimation, which allows for the imposition of biomechanical constraints without a priori knowledge regarding sensor placements. Furthermore, the algorithm estimates gyroscope and accelerometer bias, scaling, and non-orthogonality parameters in real-time. The stationary phases of the links, during which pseudo-measurements such as zero velocity or heading stabilization updates are applied, are detected using optically trained neural networks with buffered accelerometer and gyroscope...