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Intelligent Sensor Calibration: Techniques, Devices and Methodologies
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Intelligent Sensor Calibration: Techniques, Devices and Methodologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 15 December 2026 | Viewed by 11526

Special Issue Editor

Dr. Yan Liu

E-Mail Website
Guest Editor
School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: MEMS; piezoresistive sensors; piezoelectric devices; flexible sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor technology's evolution is a pivotal catalyst in the relentless march of the intelligent age. Within sensor development, calibration is a critical process that can establish correlations between sensor outputs and the measured quantities, thereby elucidating the sensor's response characteristics. As the trend in intelligence continues to expand in sensor research, an increasing number of available technologies, devices, and methods also innovate sensor calibration techniques.

This Special Issue focuses on new technologies, methods, and devices in sensor calibration, showing the latest achievements in intelligent development within this domain. Contributions about relevant theories, hardware platforms, and algorithms are warmly encouraged. Furthermore, topics that can inspire new ideas for future research are also appropriate.

The original research papers and comprehensive reviewing articles are all acceptable, but they must conform to the academic standard of this journal.

Dr. Yan Liu
Guest Editor

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

  • intelligent sensor calibration
  • calibration theory
  • calibration device
  • methodology and algorithm

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Published Papers (4 papers)

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Research

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27 pages, 4289 KB  
Article
Online Extrinsic Calibration of Camera and LiDAR Based on Cascade Optimization
by Chuanxun Hou, Zheng He, Tong Zhao, Zhenhang Guo and Xinchun Ji
Sensors 2026, 26(7), 2282; https://doi.org/10.3390/s26072282 - 7 Apr 2026
Viewed by 552
Abstract
Accurate and stable extrinsic calibration is the foundation of high-quality fusion sensing and positioning of camera and Light Detection and Ranging (LiDAR). However, traditional targetless calibration methods suffer from limitations such as poor scene adaptability and unstable convergence, which significantly restrict calibration accuracy [...] Read more.
Accurate and stable extrinsic calibration is the foundation of high-quality fusion sensing and positioning of camera and Light Detection and Ranging (LiDAR). However, traditional targetless calibration methods suffer from limitations such as poor scene adaptability and unstable convergence, which significantly restrict calibration accuracy and robustness in complex environments. Aiming at solving those problems, we propose an online cascade-optimization-based extrinsic calibration method of combining motion trajectory alignment and edge feature alignment. In the initial calibration stage, a hand–eye calibration algorithm is designed by minimizing the residual discrepancies between camera odometry and LiDAR odometry sequences. It establishes a robust initialization for subsequent optimization. Then, in order to extract robust edge line features from sparse point clouds, we employ depth difference and planar edges of point clouds in the optimization process. Subsequently, principal component analysis (PCA) is applied to compute the principal direction of the extracted line features, enabling a decoupled optimization scheme that accounts for directional observability. This approach effectively mitigates the adverse effects of uneven environmental feature distributions. Experimental validation on typical urban datasets demonstrates the method’s generalizability and competitive accuracy: rotational parameter errors are constrained within 0.25°, and translational errors are maintained below 0.05 m. This affirms the method’s suitability for high-accuracy engineering applications. Full article
(This article belongs to the Special Issue Intelligent Sensor Calibration: Techniques, Devices and Methodologies)
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21 pages, 2788 KB  
Article
Gaussian Process-Based Multi-Fidelity Bayesian Optimization for Optimal Calibration Point Selection
by Hua Zhuo, Jungang Ma, Mei Yang, Yikun Zhao, Lifang Yao, Yan Xu and Kun Yang
Sensors 2025, 25(22), 7030; https://doi.org/10.3390/s25227030 - 18 Nov 2025
Cited by 1 | Viewed by 1687
Abstract
Temperature and humidity calibration chambers, which provide controlled environments for instrument testing and validation, are widely applied in the aerospace and biomedicine fields. However, traditional fixed calibration points fail to adapt to complex operational requirements and exhibit problems including a limited coverage range [...] Read more.
Temperature and humidity calibration chambers, which provide controlled environments for instrument testing and validation, are widely applied in the aerospace and biomedicine fields. However, traditional fixed calibration points fail to adapt to complex operational requirements and exhibit problems including a limited coverage range and low efficiency. To address these challenges, this study develops a Gaussian Process-based Multi-Fidelity Bayesian Optimization (GP-MFBO) framework for optimal selection of temperature and humidity calibration points. The framework integrates the following three key components: (1) a three-layer progressive multi-fidelity modeling system comprising physical analytical models, computational fluid dynamics (CFD) numerical simulations, and experimental verification; (2) a systematic uncertainty quantification system covering model uncertainty, parameter uncertainty, and observation uncertainty; and (3) an adaptive acquisition function that balances uncertainty penalty mechanisms and multi-fidelity information gain evaluation. The experimental results demonstrate that the proposed GP-MFBO method achieves optimal calibration point combinations with a temperature uniformity score of 0.149 and humidity uniformity score of 2.38, approaching theoretical optimal solutions within 4.5% and 3.6%, respectively. Compared to standard Gaussian process, Co-Kriging, two-stage optimization, polynomial regression, and traditional single-fidelity methods, GP-MFBO achieves uniformity score improvements of up to 81.7% and 76.3% for temperature and humidity, respectively. The prediction confidence interval coverage reaches 94.2%, outperforming all comparative methods. This research provides a rigorous theoretical foundation and technical solution for the scientific design and reliable operation of large-space temperature and humidity calibration systems. Full article
(This article belongs to the Special Issue Intelligent Sensor Calibration: Techniques, Devices and Methodologies)
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21 pages, 7991 KB  
Article
Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors
by Mehmet Taştan
Sensors 2025, 25(10), 3183; https://doi.org/10.3390/s25103183 - 19 May 2025
Cited by 19 | Viewed by 5791
Abstract
Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of [...] Read more.
Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM2.5), carbon dioxide (CO2), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO2 sensor calibration, GB achieved R2 = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM2.5 sensor, kNN delivered the most successful results, with R2 = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R2 = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field. Full article
(This article belongs to the Special Issue Intelligent Sensor Calibration: Techniques, Devices and Methodologies)
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Review

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35 pages, 7608 KB  
Review
AI Methods in Sensor Calibration
by Fei Kou, Yu-Qing Liu, Chen-Xi Li, Hong-Bo Qin and Yan Liu
Sensors 2026, 26(9), 2805; https://doi.org/10.3390/s26092805 - 30 Apr 2026
Viewed by 455
Abstract
Artificial intelligence (AI)-based methods are rapidly advancing the development of sensor technology, bringing about significant advancements for sensors in structural design/optimization, fabrication, calibration and application. The recent involvement of AI models has provided a new paradigm for the calibration of sensors and greatly [...] Read more.
Artificial intelligence (AI)-based methods are rapidly advancing the development of sensor technology, bringing about significant advancements for sensors in structural design/optimization, fabrication, calibration and application. The recent involvement of AI models has provided a new paradigm for the calibration of sensors and greatly improved the accuracy and stability of obtained sensing characteristics. In this paper, we present an overview of the advances of AI methods in sensor calibration in recent years. The superiority of leveraging AI models in getting the transfer function, compensating for ambient interferences/drifts, and promoting large-scale, low-cost sensors is reviewed and discussed to illustrate the pioneering transformations in this domain. Relevant enhancing tools for data preprocessing, training optimization and data augmentation are also mentioned. The significant achievements in various sensing systems have demonstrated that AI methods can be a powerful solution to the critical issues in calibrating sensors. However, there are still several critical challenges persisting alongside these remarkable achievements, and long-term commitment remains essential for future investigations. Full article
(This article belongs to the Special Issue Intelligent Sensor Calibration: Techniques, Devices and Methodologies)
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