Challenges and Future Trends of Inertial Sensors
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: closed (1 August 2024) | Viewed by 2362
Special Issue Editor
Interests: error modeling and testing of inertial devices and calibration of strapdown inertial navigation system; transfer alignment technology of inertial navigation system and initial alignment technology of moving base; inertial based vehicle autonomous positioning and orientation technology; theory and application of high precision integrated navigation and information fusion; autonomous driving technology; robot autonomous navigation technology
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Special Issue Information
Dear Colleagues,
Inertial sensors are used in many military and civilian fields, ranging from smartphone to spacecraft. The autonomous, covert, global positioning characteristics make the inertial sensors are irreplaceable. However, as the development of new gyroscope technology, new mathematic model of navigation system, machine learning techniques, and computer vision, the conventional inertial sensors technology faces many challenges. And many new positioning requirements and sensors’ data fusion methods should be paid much attention and studied.
Some of the challenges include temperature sensitivity, noise, bias instability, and drift. To overcome these challenges, researchers are exploring new technologies such as micro-electromechanical systems (MEMS), fiber-optic gyroscopes, and quantum sensors. These technologies have the potential to improve the performance of inertial sensors and enable new applications.
The conventional inertial-based navigation system structure may not be proper for the future complex multi-source system. New algorithm and technology offer possibility to solve problems unsolvable by traditional inertial sensor technology. Meanwhile, the faced challenges also point out the future trends. For example, development of visual navigation supply new positioning reference information for 6 degrees of freedom. Deep learning can establish the complex model of inertial sensor and visual sensor. Autonomous driving enhanced inertial sensor precision positioning requirements for GNSS rejection conditions.
In the future, the inertial sensor needs to combine with other new technology to realize autonomy, intelligence, integration, high precision and high adaptability. Especially, deeply integrate inertial sensor with visual sensor to decrease cumulative error and improve stability of visual localization, and use deep learning to compensate the inertial sensors’ error.
Dr. Gongmin Yan
Guest Editor
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Keywords
- advanced measurement and error compensation
- information fusion of isomorphic and isomeric
- micro-opto-electro-mechanical-systems (MOEMS) inertial sensors
- hemispherical resonator gyroscope technology
- long-time and high-accuracy inertial navigation
- high-dynamic and high-impact inertial navigation
- deep learning used in inertial sensor modelling
- deep learning used in information fusion
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