Advanced Filtering, Fusion, and State Estimation in Microsystems and Robotics
A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 420
Special Issue Editors
Interests: kalman filtering; integrated navigation and robust filtering; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in compact, low-cost, and low-power sensors, such as MEMS, GPS, RFID, and BLE, have revolutionized smart devices, enabling new applications in navigation, robotics, and medical systems. These applications require robust state estimation and sensor fusion to mitigate noise, disturbances, and uncertainties.
Traditional methods like the Kalman filter often struggle in these complex environments, prompting the development of advanced recursive algorithms, such as unbiased FIR (UFIR), sigma point filters, and hybrid models that combine model-driven and AI-based approaches. These improvements are crucial for optimizing performance in industrial and harsh environments.
This Special Issue aims to bring together researchers and engineers to explore innovative suboptimal and robust filtering solutions, with a focus on practical algorithms for microsystems and robotics. Topics include the following:
- Model- and data-driven state estimators: Kalman, Bayesian, and AI-assisted filters.
- Multi-sensor fusion techniques.
- Performance analysis of state estimators.
- Applications in robotics, navigation, wearable devices, and autonomous systems.
We welcome theoretical and applied research addressing state estimation, sensor fusion, and control algorithms in microsystems. All manuscripts will undergo peer review.
Dr. Yuan Xu
Prof. Dr. Yuriy S. Shmaliy
Guest Editors
Manuscript Submission Information
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Keywords
- sensor fusion
- state estimation
- kalman filters
- robust filtering
- navigation
- robotics
- wearable devices
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