Advanced Sensors and Sensing Technologies for Indoor Localization
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Physics General".
Deadline for manuscript submissions: closed (10 November 2021) | Viewed by 21693
Special Issue Editors
Interests: battery management; instrumentation and measurement; positioning systems (using magnetic field and ultrasound); statistical signal processing; system identification; sensors
Special Issues, Collections and Topics in MDPI journals
Interests: battery management; electronics; positioning systems; OLED; photovoltaic; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Accurate information about the position of users, devices, mobile robots, or systems is crucial for the safe and efficient operation of applications in the industrial, commercial, and consumer fields. High-accuracy short-range localization is important also for specialized applications such as hand and finger tracking for biomedical or telemanipulation scenarios.
Particular research interest is devoted to those environments where the availability and accuracy of global navigation satellite systems is challenging, such as indoors. In such environments, positioning may be achieved by a wide array of sensor technologies and processing methods.
This Special Issue aims to gather contributions on sensors for localization, which may include, but are not limited to, radio-frequency, magnetic-field, imaging, inertial, acoustic, and ultrasound sensors. Furthermore, the scope of this Special Issue covers also sensing and processing methods applied to the positioning problem. These include robust numerical methods, optimization strategies, tracking algorithms, machine learning, and performance characterization and validation methods.
Prof. Dr. Alessio De Angelis
Dr. Francesco Santoni
Guest Editors
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Keywords
- Indoor positioning
- Radio-frequency localization
- Magnetic-field sensors
- Ultrasound positioning sensors and systems
- Tracking
- Machine learning for positioning
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