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- MakaleAçık Erişim2025Scopus SJRQ2Multi-sensor data fusion based on the similarity measure and belief (Deng) entropy under neutrosophic evidence sets
Köseoğlu, Ali; Şahin, Rıdvan; Demir, Ümit
The Dempster–Shafer evidence theory is a very practical concept for handling uncertain information. The foundation of this theory lies in the basic probability assignment (BPA), which exclusively accounts for the degree of support attributed to focal elements (FEs). In this study, neutrosophic evidence sets (NESs) are defined to introduce additional probabilistic measures, aimed at addressing the uncertainty, imprecision, incompleteness, and inconsistency present in real-world information. The basic element of NESs is a neutrosophic basic probability assignment (NBPA), which consists of three components. The truth degree of FEs is represented by the first BPA, the second BPA represents the indeterminacy degree of FEs, and the last BPA characterizes the falsity degree of FEs. In NESs, each support degree of FEs is shown separately without any limitation. Therefore, the general concept of NESs is broader compared to traditional evidence sets and intuitionistic fuzzy evidence sets. Unlike the neutrosophic set (NS), the NBPA method assigns truth-support, uncertainty-support, and false-support degrees, as well as these support degrees, to single and multiple subsets in a discriminative framework. This paper aimed to develop some information measures for NESs, such as neutrosophic Deng entropy (NDE), neutrosophic cosine similarity measure, and neutrosophic Jousselme distance. Then, an improved method based on NDE and neutrosophic cosine similarity measure was established to combine contradictory evidence to increase the influence of reliable evidence on the one hand and to reduce the influence of unreliable evidence on the other hand. Finally, a case involving sensor data integration for target identification was studied to highlight the importance of these innovative ideas. The numerical example demonstrates that the proposed method provides more reliable and superior fusion performance compared to classical models, particularly in scenarios involving high conflict and uncertain information. However, the effectiveness of the method is partially influenced by the structure of the similarity matrix and the entropy parameters, which necessitates careful parameter tuning to achieve optimal results. These limitations are explicitly highlighted to serve as a guide for future improvements and broader applications of the method.
AtıfScopus1WoS1OpenAlex1
İncele - MakaleYalnız künye2019Scopus SJRQ1Thermal Multisensor Fusion for Collaborative Robotics
Benli, Emrah; Spidalieri, Richard Lee; Motai, Yuichi
Collaborative robotic configurations for monitoring and tracking human targets have attracted interest in the fourth industrial revolution. The fusion of different types of sensors embedded in collaborative robotic systems achieves high-quality information and contributes to significantly improve robotic perception. However, current methods have not deeply explored the capabilities of thermal multisensory configurations in human-oriented tasks. In this paper, we propose thermal multisensor fusion (TMF) for collaborative robots to overcome the limitations of stand-alone robots. Thermal vision helps to utilize the heat signature of the human body for human-oriented tracking. An omnidirectional (O-D) infrared (IR) sensor provides a wide field of view (FOV) to detect human targets, and Stereo IR helps determine the distance of the human target in the oriented direction. The fusion of O-D IR and Stereo IR also creates a multisensor stereo for an additional determination of the distance to the target. The fusion of thermal and O-D sensors brings their limited prediction accuracy with their advantages. The maximum a posteriori method is used to predict the distance of the target with high accuracy by using the distance results of TMF stereo from multiple platforms according to the reliability of the sensors rather than its usage of visible-band-based tracking methods. The proposed method tracks the distance calculation of each sensor instead of target trajectory tracking as in visible-band methods. We proved that TMF increases the perception of robots by offering a wide FOV and provides precise target localization for collaborative robots.
AtıfScopus20WoS19OpenAlex21İncele