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 382

Special Issue Editors

School of Electrical Engineering, University of Jinan, Jinan 250022, China
Interests: kalman filtering; integrated navigation and robust filtering; signal processing
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Guest Editor
Department of Electronics Engineering, Universidad de Guanajuato, Salamanca 36855, Guanajuato, Mexico
Interests: FIR filters; kalman filters; global positioning system; inertial navigation; mobile robots; sensor fusion; state estimation

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

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Keywords

  • sensor fusion
  • state estimation
  • kalman filters
  • robust filtering
  • navigation
  • robotics
  • wearable devices

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Published Papers (1 paper)

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Research

13 pages, 1063 KiB  
Article
Trajectory Tracking Using Cumulative Risk–Sensitive Finite Impulse Response Filters
by Yi Liu and Shunyi Zhao
Micromachines 2025, 16(4), 365; https://doi.org/10.3390/mi16040365 - 22 Mar 2025
Viewed by 216
Abstract
Trajectory tracking is a critical component of autonomous driving and robotic motion control. This paper proposes a novel robust finite impulse response (FIR) filter for linear time-invariant systems, aimed at enhancing the accuracy and robustness of trajectory tracking. To address the limitations of [...] Read more.
Trajectory tracking is a critical component of autonomous driving and robotic motion control. This paper proposes a novel robust finite impulse response (FIR) filter for linear time-invariant systems, aimed at enhancing the accuracy and robustness of trajectory tracking. To address the limitations of infinite impulse response (IIR) filters in complex environments, we integrate a cumulative risk–sensitive criterion with an FIR structure. The proposed filter effectively mitigates model mismatches and temporary modeling uncertainties, making it highly suitable for trajectory tracking in dynamic and uncertain environments. To validate its performance, a comprehensive vehicle trajectory tracking experiment is conducted. The experimental results demonstrate that, compared to the Kalman filter (KF), risk–sensitive filter (RSF), and unbiased FIR (UFIR) filter, the proposed algorithm significantly reduces the average tracking error and exhibits superior robustness in complex scenarios. This work provides a new and effective solution for trajectory tracking applications, with broad potential for practical implementation. Full article
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