Special Issue on Multi-sensor Measurement and Data Fusion

Status Canceled
Call for papers December 05, 2022
Expected publication September 30, 2023
Submission deadline August 15, 2023
Paper length >10 pages


In today's society, there is a growing need for reliable and comprehensive measurements of critical quantities. Since no single sensor or measurement can reflect the overall characteristics of an object, the use of multiple sensors becomes critical. The ability to process multi-sensor measurement data is also very important. Data fusion, also known as information fusion, produces more consistent, accurate and reliable information by combining data from multiple sources. Rather than just enabling low-level output, data fusion facilitates the flow of information from raw data to high-level understanding and insights as evidence for industry decision-making.

Multi-sensor measurement and data fusion include multi-sensor measurement and data fusion technology, introducing different architectures of multi-sensor process monitoring systems and different, widely used data fusion algorithms. Data fusion technology has become one of the research hotspots in the world today. Using specific criteria, data fusion can obtain a consistent interpretation of the measured object, allowing multimodal sensor systems to perform better than many unimodal systems.

The development of such systems has been growing rapidly in recent decades and has a wide range of applications in various fields such as industry, healthcare, and environmental protection. This special issue on "Multisensor Measurements and Data Fusion" focuses on recent advances in the development and application of multisensor systems.

Potential topics

Include but are not limited to the following:
  • Multi-sensor measurement and data fusion technology for manufacturing process monitoring
  • Wireless multimodal sensor networks
  • Multimodal measurement system
  • n-dimensional data fusion
  • AI-enabled industrial measurement
  • Modeling and simulation of different types of sensors
  • Ad hoc wireless multi-sensor measurement system
  • Multi-source measurement signal fusion based on neural network
  • Deep learning methods for data processing and feature extraction
  • New applications of multi sensor measurements and data fusion from all fields and all other related fields


Zhengyi Chai
Managing Editor
Prof. Zhengyi Chai
Tiangong University, China
Mohit Angurala
Dr. Mohit Angurala
Khalsa College of Engineering and Technology, India