Signal Processing, Sensor Fusion and Data Fusion in Technical Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 20 October 2025 | Viewed by 23
Special Issue Editor
Interests: measurements of physical quantities; phase angle measurements; WIM systems and measurement of road traffic parameters; modeling and simulations of measurement systems; signal processing and data fusion in measurement systems
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Special Issue Information
Dear Colleagues,
In any technical system, the cooperation of sensors and measuring systems enables the collection of information about the current state of the tested object. The further processing and use of the object depend upon the credibility and accuracy of these data. In the case of important phenomena and objects, we are increasingly using many different sensors and other types of knowledge. Therefore, it has become extremely important to utilize all available data wisely. Since the early 2000s, data fusion techniques have been intensively developed at various levels of the data acquisition and processing process.
In the case of measurement systems, the basic aim of data fusion is to reduce the uncertainty of the measurement results or to increase the effectiveness of the classification, detection, or location of the object. This involves the joint use of signals and measurement data from many sensors, as well as information derived from other sources (e.g., apriori knowledge). The method used to combine this information depends on the specific characteristics of the object being measured and the measurement tools employed. This method can take diverse forms, starting with the simple averaging of results and continuing on to the use of models based on, for e.g., Bayes theory or Kalman’s filtration theory.
In technical systems, the fusion process can be located at different levels, from the hardware to advanced signal processing. The methods used for the fusion of data can be divided into three groups:
- Competitive fusion, where different types of sensors are used to measure the same physical quantity. This may lead to information redundancy.
- Complementary fusion, where each sensor is used to measure a different property of the studied object.
- Cooperative fusion, where the correct operation of a single sensor is dependent on the results of some other sensor. Without cooperation, the operation of the first sensor would be impossible or undesirable.
Therefore, it is extremely important to review possible data fusion methods, hardware solutions that enable the desired fusion, and examples of the specific application of such solutions.
This Special Issue welcomes the submission of papers that present innovative studies on fusion in technical systems. The scope of this Special Issue includes the following topics:
- Sensor level fusion
- Data fusion methods
- Application of fusion in measurement systems
- Data fusion in WIM systems
- Multi-sensor data fusion
- Signal processing in technical and biomedical system
Prof. Dr. Ryszard Sroka
Guest Editor
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Keywords
- data fusion
- information fusion
- data fusion in WIM
- sensor fusion
- signal processing
- technical systems
- biomedical systems, fusion methods
- data fusion applications
- multi-sensor fusion
- sensor networks
- vehicle sensor fusion
- robot sensor fusion
- sensor fusion algorithms
- smart sensors
- applications of sensor fusion
- multimodal sensor fusion
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