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. The goal of data fusion is to reduce uncertainty and improve the quality of predictions or insights derived from the data.

There are two main types of data fusion: direct and indirect. Direct fusion involves combining data from various sensors, which can be either homogeneous (similar types) or heterogeneous (different types). Indirect fusion, on the other hand, relies on prior knowledge and human or environmental inputs to inform the data integration process. For example, assessing the freshness of food may require not just visual inspection but also sensory inputs like smell and touch, along with contextual knowledge about storage conditions ³.

Data fusion is widely applied in various fields, including autonomous driving, location detection with GPS, and many engineering applications, making it a critical component in modern data analysis and decision-making processes ³.