Special Issue "Gyroscopes and Accelerometers"
Deadline for manuscript submissions: closed (20 September 2019).
Inertial sensors, such as gyroscopes and accelerometers, are important components of inertial measurement units (IMUs). Inertial sensors provide an output that is proportional to angular velocity or angular acceleration. The output signal is corrupted by additive noise plus a random drift component. Many modeling, sensor fusion and calibration techniques are used to estimate and remove sensor bias. The objective of this Special Issue is to present significant work in this field. Papers will address wide range of applications such as gyroscope and accelerometer error modeling, modeling of array of low-cost sensors, calibration, bias estimation with Kalman filter or other techniques, GPS and/or other long-term bias-free integration methods with IMUs.
Dr. Ahmed Zaki
Prof. Richard J. Vaccaro
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Gyroscope and accelerometer error modeling
- Array of low-cost sensors
- Sensor fusion algorithms
- Integration with long-term sensors
- Ground and transfer alignment algorithms for bias estimation