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Sensor Fusion: Kalman Filtering for Engineering Applications
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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: 25 June 2026 | Viewed by 3688

Special Issue Editors

Dr. Xinhe Zhu

E-Mail Website
Guest Editor
School of Engineering, RMIT University, Melbourne, Australia
Interests: vehicular navigation; INS/GNSS integration; cubature Kalman filter; abnormal observations identification; Mahalanobis distance criterion
Dr. Yongmin Zhong

E-Mail Website
Guest Editor
School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
Interests: robotic minimally invasive surgery; surgical simulation; soft tissue characterization and modeling; virtual reality and haptics; sensing and measurement; mobile robots; robotic path planning; optimal estimation; information filtering; multi-sensor data fusion; integrated navigation system; vehicle navigation
Special Issues, Collections and Topics in MDPI journals
Dr. Bingbing Gao

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

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. Sensors is an international peer-reviewed open access semimonthly 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 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 (4 papers)

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Research

19 pages, 865 KB  
Article
Research on the Control Algorithm for a Brushless DC Motor Based on an Adaptive Extended Kalman Filter
by Tong Jinwu, Zha Lifan, Lu Xinyun, Li Peng, Sun Jin and Liu Shujun
Sensors 2026, 26(3), 1050; https://doi.org/10.3390/s26031050 - 5 Feb 2026
Abstract
To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive [...] Read more.
To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC. Full article
(This article belongs to the Special Issue Sensor Fusion: Kalman Filtering for Engineering Applications)
24 pages, 2735 KB  
Article
Hierarchical Data Fusion Algorithm for Multiple Wind Speed Sensors in Anemometer Tower
by Junhong Duan, Hailong Zhang, Chao Tu, Jun Song, Wei Niu, Zhen Zhang, Jinze Han and Jiuyuan Huo
Sensors 2026, 26(2), 565; https://doi.org/10.3390/s26020565 - 14 Jan 2026
Viewed by 221
Abstract
Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-precision wind speed information. In this study, a hierarchical [...] Read more.
Accurate and reliable wind speed measurement is essential for applications such as wind power generation and meteorological monitoring. Data fusion from multiple anemometers mounted on wind measurement towers is a key approach to obtaining high-precision wind speed information. In this study, a hierarchical data fusion strategy is proposed to enhance both the quality and efficiency of multi-sensor fusion on wind measurement towers. At the local fusion stage, multi-sensor wind speed data are denoised and fused using an unscented Kalman filter enhanced with fuzzy logic and a robustness factor (FLR-UKF). At the global decision fusion stage, decision-level fusion is achieved through an extreme learning machine (ELM) neural network optimized by a Q-learning-improved Aquila optimizer (QLIAO-ELM). By incorporating a spiral surrounding attack mechanism and a Q-learning-based adaptive strategy, QLIAO-ELM significantly enhances global search capability and convergence speed, enabling the ELM network to obtain superior parameters within limited computational time. Consequently, the accuracy and efficiency of decision fusion are improved. Experimental results show that, during the local fusion phase, the RMSE of FLR-UKF is reduced by 26.46% to 28.6% compared to the traditional UKF; during the global fusion phase, the RMSE of QLIAO-ELM is reduced by 27.1% and 14.0% compared to ELM and ISSA-ELM, respectively. Full article
(This article belongs to the Special Issue Sensor Fusion: Kalman Filtering for Engineering Applications)
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23 pages, 2364 KB  
Article
An Improved Variational Bayesian-Based Adaptive Federated Kalman Filter for Multi-Sensor Integrated Navigation Systems
by Yuwei Yan and Jing Yang
Sensors 2025, 25(23), 7173; https://doi.org/10.3390/s25237173 - 24 Nov 2025
Viewed by 854
Abstract
Efficient fusion of navigation sensor data with different output frequencies and data types is critical for ensuring that vehicle-mounted integrated navigation systems consistently provide stable, reliable navigation solutions in complex dynamic operational environments. To address the degradation of estimation accuracy caused by the [...] Read more.
Efficient fusion of navigation sensor data with different output frequencies and data types is critical for ensuring that vehicle-mounted integrated navigation systems consistently provide stable, reliable navigation solutions in complex dynamic operational environments. To address the degradation of estimation accuracy caused by the noise characteristics mismatch of sensor measurement, an information fusion framework based on federated Kalman filter (FKF) framework is designed by incorporating an improved variational Bayesian-based adaptive Kalman filter (IVBAKF) as the core estimation module of local filters. IVBAKF mitigates the impact of uncertain measurement noise from navigation sensors through effectively estimating the measurement noise covariance matrix (MNCM) by leveraging an adaptive forgetting factor. The adjustment strategy for the forgetting factor employs a predefined mapping function derived from the squared Mahalanobis distance (SMD) of the measurement innovation, which serves as an indicator for detecting anomalies in measurement noise within the FKF, thereby enhancing the tracking capability for the MNCMs. The effectiveness of the proposed algorithm is validated through Monte Carlo simulation-based comparative experiments. The simulation results demonstrate that compared to the FKF-based baseline algorithm with nominal covariance matrices, the proposed algorithm achieves an average reduction of 43.21% in the Root Mean Square Errors (RMSEs) of the estimated navigation parameters in scenarios characterized by uncertain and time-varying measurement noise. Thus, the robustness of the proposed algorithm against complex measurement noise conditions is verified. Full article
(This article belongs to the Special Issue Sensor Fusion: Kalman Filtering for Engineering Applications)
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18 pages, 1927 KB  
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
Cited by 3 | Viewed by 1090
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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