Inertial Sensors

Dear Colleagues,

Inertial sensors have become a key enabling technology in a large variety of applications, and are currently found in almost every digital device and every intelligent vehicle. Inertial sensor information is, for instance, combined with absolute position measurements to facilitate localization, navigation, and mapping. Wearable inertial sensors are also frequently used in health care and sports applications for capturing movement patterns outside of typical laboratory and clinical environments. Many other application domains and examples can be found.

At the same time, inertial measurements are known to be corrupted by errors such as sensor bias, noise, and misalignment. Because of this, integration of the sensor signals to position and orientation results in integration drift. Inertial sensor measurements are therefore typically combined with additional information, such as from additional sensor measurements or additional models. Advanced sensor fusion and state estimation methods are being developed to combine this information and minimize the influence of the sensor errors on the motion states of interest. Statistical filters and state estimation techniques are commonly used. Machine learning methods have recently emerged as a promising new direction.

This Special Issue aims to gather novel developments in the use of inertial sensors, including both recent methodological developments and new results in applications of inertial sensors. Given the focus on methodological developments, we strongly encourage authors to deposit their source code in a public repository (e.g., GitHub) if possible. Topics include but are not limited to the following keywords.

Dr. Thomas Seel
Dr. Manon Kok
Dr. Ryan McGinnis
Guest Editors

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