FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

The main goal of information fusion is to combine heterogeneous information to obtain a single composite of potential comparable alternative solutions that can be classified and ranked. The crux of information fusion, which is a type of data fusion, is threefold: (i) data must be comparable and numerical, using some normalization process; (ii) imprecision in data must be taken into consideration; (iii) an appropriate aggregation function to combine values into a single score must be selected.Recently, computational intelligence concepts and techniques to perform data/information fusion are emerging as suitable tools. Although with a different perspective, another field where much work has also been done for combining heterogeneous information is multi-criteria decision-making. In general, multi-criteria problems are modelled by choosing a set of relevant criteria – usually dealing with heterogeneous data – that have to be aggregated (i.e. fused) to obtain a single rating for each candidate alternative.In this paper we propose an algorithm for data/information fusion, which includes concepts from multi-criteria decision-making and computational intelligence, specifically, fuzzy multi-criteria decision-making and mixture aggregation operators with weighting functions. The application field of interest for this work is safe spacecraft landing with hazard avoidance; hence two existing hazard maps will be used to illustrate the versatility of the algorithm.