## 2 Methods for Industrial Data Fusion at Sensor, Feature and Decision Level
Regarding the _feature level_, unsupervised learning algorithms such as K-means clustering or self-organizing maps can be used to classify relationships between entities and extract further information. These methods are particularly suitable for applications where patterns or geometrical relations need to be detected. Further methods to address pattern recognition problems are adaptive resonance theory (ART), ARTMAP or Fuzzy ART. For applications with limited bandwidth, [PERSON] et al. [17] applied support vector machines (SVM) to compress the information in sensor fusion for large amounts of samples. Further algorithms and their applications are presented by [PERSON] et al. [18]. Depending on the use case and its requirements, the suitability of the data fusion algorithm must be evaluated individually. Thereby, the target application defines the required accuracy, computational complexity, processing power and available amount of data. [PERSON] et al. [16] introduced a classification depending on the degree of complexity. Thereby, low-complexity algorithms such as k-means allow for an enhanced battery life for wearable health monitoring systems due to their reduced computational complexity and resource requirements. In Table 1, the presented algorithms are distinguished regarding their application or abstraction level as sensor fusion, feature fusion and decision fusion. In general, there is a wide range of possible data fusion applications. Typical examples of the military industry are the location and tracking of moving objects. Depending on the target, multiple sensor platforms are used, and various sources as acoustic signals or electromagnetic radiation can be observed [38]. More recent applications are in the health care [39] or telecommunication sectors as wireless sensor networks. The widespread machine learning algorithms such as ANN, SVM or GMM lead to an increased adoption in sensor fusion approaches and enhance the possibilities of sensor fusion. However, the efficient handling of big amounts of data in industrial applications remains an important challenge for the future. Approaches called consensus filters try to address this issue by implementing distributed filters to create scalable algorithms [40; 41]. More recent developments were published in the automotive sector, where fast develop \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abstraction Level** & **Method** & **Applications/References** \\ \hline \multirow{4}{*}{Sensor Level} & Weighted average & [19] \\  & Kalman Filter (EKF, UKF) & [20; 21] \\  & Particle Filtering & [22; 23] \\  & Dempster-Shafer method & [24; 25] \\ \hline \multirow{4}{*}{Feature level} & k-nearest neighbour (k-NN), k-means & [26] \\  & Decision Trees & [27] \\ \cline{1-1}  & Support vector machines (SVM) & [28] \\ \cline{1-1}  & Artificial neural networks (ANN) & [29; 30; 31] \\ \cline{1-1}  & Gaussian mixture model (GMM) & [32; 33] \\ \hline \multirow{2}{*}{Decision level} & Bayesian inference & [34; 35] \\ \cline{1-1}  & Fuzzy logic & [36; 37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of data fusion algorithms at sensor, feature and decision level. ments are noticeable. Future self-driving or autonomous vehicles require reliable information about their environment and are therefore predestined for sensor fusion [42; 43; 44]. [PERSON] et al. [45] fusioned LiDAR and a vision system to reduce the false positive detection of the LiDAR system utilizing neural networks to project the camera to the LiDAR space. A more detailed review in this sector is presented by [PERSON] et al. [42] and [PERSON] et al. [44]. These developments will probably lead to further adoption and improvements in manufacturing industries. Besides data-related issues, the interaction and data fusion of human inputs and machine data will play an important role in the future and requires more research to be developed in the near future.