Sensor Fusion: Kalman Filtering for Engineering Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 25 April 2025 | Viewed by 244
Special Issue Editors
Interests: vehicular navigation; INS/GNSS integration; cubature Kalman filter; abnormal observations identification; Mahalanobis distance criterion
Interests: aerial/ground vehicle navigation, guidance and control; optimal estimation and control of vehicle dynamics; error/uncertainty analysis and compensation; multi-sensor integrated vehicle navigation; vehicle motion planning
Interests: multi-sensor technology; navigation and control technology; intelligent sensing and robot positioning technology; information fusion; artificial intelligence
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Special Issue Information
Dear Colleagues,
The fusion of data from multiple sensors is a critical aspect of modern systems, enhancing their precision, reliability, and robustness. Kalman filtering is one of the most widely used techniques for integrating multisensory data, providing optimal state estimation for dynamic systems. With advancements in sensor technology and computational power, sensor fusion based on Kalman filtering has become essential in fields such as computational biology, medicine, autonomous navigation, intelligent systems and robotics.
This Special Issue will bring together original research and review articles on the latest developments, applications, and challenges in sensor fusion with a focus on Kalman filtering for biomedical and engineering applications. We welcome contributions that explore theoretical advancements, practical implementations, and innovative solutions related to this rapidly evolving topic.
Potential topics include, but are not limited to, the following:
- Kalman filter-based sensor fusion for navigation and control;
- Sensor fusion in autonomous vehicles and robotics;
- Real-time sensor integration for biomedical systems;
- GPS and inertial measurement unit (IMU) fusion using Kalman filters;
- Kalman filters for identification of nonlinear systems;
- Adaptive Kalman filtering for dynamic environment;
- Multisensor-based route planning and haptic control;
- Applications of Kalman filtering in surgical simulation, soft tissue deformation, and biological information processing;
- Optimizations in computational biology, medicine, and diagnostics.
Dr. Xinhe Zhu
Dr. Yongmin Zhong
Dr. Bingbing Gao
Guest Editors
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Keywords
- Kalman filter
- sensor fusion
- multiple sensors
- autonomous navigation
- GPS and IMU fusion
- biomedical systems
- computational biology
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