Special Issue "Signal Processing, Sensor Fusion, and Data Fusion in Measurement Systems"
Deadline for manuscript submissions: 10 November 2023 | Viewed by 2245
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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In any technical system, the cooperation of sensors and measuring systems is essential element enabling the collection of information about the current state of the tested object. The credibility and accuracy of these data are crucial for its further processing and use. Increasingly, in the case of important phenomena and objects, we simultaneously use many different sensors and other types of knowledge resources. Therefore, it has become extremely important to use all available data wisely. Since the early 2000s, methods of data fusion at various levels of the data acquisition and processing process have been intensively developed worldwide.
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 idea involved the joint use of signals and measurement data from many sensors and information derived from other sources (e.g., apriori knowledge). The method used to combine this information depends on the specifics of the object being measured and on the used measurement tools. 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 measurement systems, the fusion process can be located at different levels, from the hardware to advanced methods of 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 from the point of view of measurement systems to review possible data fusion methods, hardware solutions enabling the desired fusion, as well as examples of specific applications of such solutions.
This Special Issue invites papers presenting innovative works on all aspects related to fusion in measurement systems, especially in the following areas:
- Sensor level fusion
- Data fusion methods
- Application of fusion in measurement systems
- Data fusion in WIM systems
- Multi-sensor data fusion
- Signal processing in measurement system
Prof. Dr. Ryszard Sroka
Manuscript Submission Information
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- data fusion
- information fusion
- data fusion in WIM
- sensor fusion
- signal processing
- measurement 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