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Micromachines
Special Issues
Advanced Filtering, Fusion, and State Estimation in Microsystems and Robotics
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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 January 2026 | Viewed by 2122

Special Issue Editors

Dr. Yuan Xu

E-Mail Website
Guest Editor
School of Electrical Engineering, University of Jinan, Jinan 250022, China
Interests: kalman filtering; integrated navigation and robust filtering; signal processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuriy S. Shmaliy

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Published Papers (3 papers)

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Research

19 pages, 3517 KB  
Article
Student’s t-Distributed Extended Kalman Filter with Switch Factor for UWB Localization Under Colored Measurement Noise
by Yuan Xu, Haoran Yin, Maosheng Yang, Lei Deng and Mingxu Sun
Micromachines 2025, 16(11), 1231; https://doi.org/10.3390/mi16111231 - 29 Oct 2025
Viewed by 738
Abstract
To increase information accuracy when using ultrawide-band (UWB) localization for robotic dogs, we introduce a switching method for a Student’s t-distributed extended Kalman filter (EKF) that achieves UWB localization under colored measurement noise (CMN). First, a distributed UWB localization framework under CMN [...] Read more.
To increase information accuracy when using ultrawide-band (UWB) localization for robotic dogs, we introduce a switching method for a Student’s t-distributed extended Kalman filter (EKF) that achieves UWB localization under colored measurement noise (CMN). First, a distributed UWB localization framework under CMN is designed, which can reduce the impact of CMN caused by carrier jitter on positioning accuracy. Then, a Student’s t-distributed EKF under CMN with a switch factor is proposed, which effectively improves the adaptability of the algorithm through adaptive selection of colored factors. Finally, experimental validation demonstrates the efficacy and high performance of the proposed method for two practical scenarios. Full article
(This article belongs to the Special Issue Advanced Filtering, Fusion, and State Estimation in Microsystems and Robotics)
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36 pages, 7149 KB  
Article
An Improved Cubature Kalman Filter for GNSS-Denied and System-Noise-Varying INS/GNSS Navigation
by Di Liu, Xiyuan Chen and Bingbo Cui
Micromachines 2025, 16(10), 1116; https://doi.org/10.3390/mi16101116 - 29 Sep 2025
Viewed by 521
Abstract
The degradation of nonlinear filtering in INS/GNSS integrated navigation due to missing GNSS observations and system noise uncertainty is addressed in this paper. An improved cubature Kalman filter (ICKF) is proposed, leveraging a modified cubature point update framework (MUF) and the maximum likelihood [...] Read more.
The degradation of nonlinear filtering in INS/GNSS integrated navigation due to missing GNSS observations and system noise uncertainty is addressed in this paper. An improved cubature Kalman filter (ICKF) is proposed, leveraging a modified cubature point update framework (MUF) and the maximum likelihood (ML) principle. In the ICKF, the ML principle is employed to estimate the process noise covariance, which is then integrated into the MUF to construct the posterior cubature points directly, bypassing the need for resampling. As the process noise covariance is updated in real time, and the prediction cubature points’ error is directly transferred to the posterior cubature points, the proposed algorithm demonstrates reduced sensitivity to missing observations and system noise uncertainty. The effectiveness of the proposed algorithm has been validated through both simulation and practical experiments. Full article
(This article belongs to the Special Issue Advanced Filtering, Fusion, and State Estimation in Microsystems and Robotics)
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13 pages, 1063 KB  
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 464
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
(This article belongs to the Special Issue Advanced Filtering, Fusion, and State Estimation in Microsystems and Robotics)
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