Battlespace Situation Assessment via Clustering and Case-Based Reasoning
نویسندگان
چکیده
We cluster surface target feature vectors by position in a certain area of the battlespace and make inventories of the resulting clusters by type and count. The feature vectors come from the target tracks. Our new centralized-mean clustering method is robust. Next, we apply case-based reasoning to infer the enemy unit types and their posture for situation awareness. We then employ a weighted retrieval process to match the new cluster inventories to cases whose solutions provide unit types.
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