In the process of autonomous combat and intelligent decision-making of Unmanned Aerial Vehicles (UAVs), the similarity measure of the new situation and historical situation acquired by drones is an important link in situation assessment and operational decision-making. However, the existing similarity measurement methods mainly deal with discrete moments. The Euclidean distance method used in the existing methods shows low processing efficiency and is data-sensitive that does not fit the characteristics of the combat posture. To solve this problem, a multi-granularity UAV situation similarity measure method is proposed. This method first selects the UAV combat posture elements and characterizes the data in time series. Then, the empirical modality decomposition method is used to extract sequence trends of historical experience and new situation to measure their similarity. Finally, the Self-Organization Map (SOM) clustering is performed for each situation sequence with similar trend, gaining sets formed by a number of cluster centers. Using the optimal subpattern assignment distance to measure the distance between the sets, we extract the part with a small set distance to obtain a historical experience similar to the new situation. The comparison of the classification effect of common datasets and the similarity measurement experiment of the combat simulation situation shows that the proposed method can effectively measure the degree of similarity between two sequences with a good measurement effect. The classification accuracy in the classification experiment is 18% higher than the traditional method. The method is simple and has certain practical values.
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