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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: 31 October 2025 | Viewed by 991

Special Issue Editors

School of Engineering, RMIT University, Melbourne, Australia
Interests: vehicular navigation; INS/GNSS integration; cubature Kalman filter; abnormal observations identification; Mahalanobis distance criterion

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Guest Editor
Department of Mechanical and Automotive Engineering, RMIT University, Melbourne, VIC 3000, Australia
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

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Guest Editor
School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
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

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • Kalman filter
  • sensor fusion
  • multiple sensors
  • autonomous navigation
  • GPS and IMU fusion
  • biomedical systems
  • computational biology

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Published Papers (1 paper)

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Research

18 pages, 1927 KiB  
Article
An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS Systems
by Jiahao Zhang, Kaiqiang Feng, Jie Li, Chunxing Zhang and Xiaokai Wei
Sensors 2025, 25(11), 3483; https://doi.org/10.3390/s25113483 - 31 May 2025
Viewed by 363
Abstract
Aimed at the problem of navigation performance degradation in inertial navigation system/global navigation satellite system (INS/GNSS)-integrated navigation systems due to measurement anomalies and non-Gaussian measurement noise in complex navigation environments, an adaptive unscented Kalman filter (AUKF) algorithm based on the maximum versoria criterion [...] Read more.
Aimed at the problem of navigation performance degradation in inertial navigation system/global navigation satellite system (INS/GNSS)-integrated navigation systems due to measurement anomalies and non-Gaussian measurement noise in complex navigation environments, an adaptive unscented Kalman filter (AUKF) algorithm based on the maximum versoria criterion (MVC) is developed. The proposed method is designed to enhance INS/GNSS-integrated navigation system robustness and accuracy by addressing the limitations of conventional filtering approaches. An adaptive unscented Kalman filter is constructed to enable dynamic adjustment of filter parameters, allowing for real-time adaptation to measurement anomalies. This ensures accurate tracking of navigation parameter states, thereby improving the robustness of the INS/GNSS-integrated navigation system in the presence of abnormal measurements. On this basis, fully considering the high-order moments of estimation errors, the maximum versoria criterion is introduced as the optimization criterion to construct a novel cost function, further effectively suppressing deviations caused by non-Gaussian disturbances and improving system navigation accuracy. The effectiveness of the proposed method was verified through vehicle navigation experiments. The experimental results demonstrate that the proposed method outperforms traditional approaches, effectively handling measurement anomalies and non-Gaussian measurement noise while maintaining robust navigation performance. Specifically, compared to the EKF, UKF, and MCCUKF, the proposed method reduces the root mean square error of velocity and position by over 60%, 50%, and 30%, respectively, under complex navigation conditions. The algorithm exhibits good accuracy and stability in complex environments, showcasing its practical applicability in real-world navigation systems. Full article
(This article belongs to the Special Issue Sensor Fusion: Kalman Filtering for Engineering Applications)
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