Data fusion refers to the process of integrating data from multiple sources to produce more accurate and reliable information. This technique is particularly useful in scenarios where data from different sensors or instruments is combined to enhance the understanding and interpretation of that data. For instance, in engineering applications, data fusion can improve location detection using GPS or facilitate autonomous driving by merging data from various sensors ³.

There are two primary types of sensor fusion: direct and indirect. Direct fusion involves combining data from various sensors directly, while indirect fusion relies on prior knowledge and human or environmental input to interpret the data more effectively. An example of indirect fusion could be assessing the freshness of food by combining visual, olfactory, and tactile information ³.

In the context of data science, data fusion is essential for creating cohesive datasets that can be used for analysis, visualization, and decision-making ¹.

If you have more specific questions about data fusion or its applications, feel free to ask!