@thesis{Hudhud-Mughrabi24-B-Kalman,
  title = {Kalman Filter Approach to Sensor Fusion of Ultra-Wideband Positioning and {IMU} Readings for Enhanced Indoor Tracking of Collaborating Humans},
  abstract = {The question of how humans collaborate to perform complex tasks such as surgery has previously been investigated via multimodal sensing and analysis. Ultra-wideband (UWB) localization systems can be deployed to track collaborating team members due to good maneuverability even in cramped environments. However, UWB systems' sampling rate is inversely proportional to the number of people tracked, and their accuracy is hindered by electromagnetic occlusion. This thesis combines UWB positioning with measurements from a wearable inertial measurement unit (IMU) by applying an error-state extended Kalman filter (ES-EKF) to improve position and orientation estimation during team collaborative studies. ES-EKF offers faster and more consistent estimation and can be estimated even without UWB input. Single-human and multi-human sessions were recorded and filtered for evaluation in comparison to ground truth from optical motion capture. By integrating the IMU, the ES-EKF increases the sampling rate from 0.5–20 Hz to 100 Hz. As it is corrected in only 2 degrees of freedom (DOF), the ES-EKF yields improved results over UWB in 4 out of 6 DOF: lateral and longitudinal position and yaw and pitch orientation. Further filter design implications are suggested for future application of ES-EKF in position and orientation estimation of collaborating humans.},
  degree_type = {Bachelor},
  school = {Kadir Has University},
  address = {Istanbul, Turkey},
  month = jun,
  year = {2024},
  note = {Bachelor of Science (BSc) in Mechatronics Engineering},
  slug = {hudhud-mughrabi24-b-kalman},
  author = {Hudhud Mughrabi, Moaaz},
  month_numeric = {6}
}