Name
Abstract | ||
|---|---|---|
Abstract. 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 aweighted,retrieval process to match ,the new cluster inventories to cases whose solutions provide unit types. |
Year | Venue | Keywords |
|---|---|---|
2002 | Computers and their applications | situation awareness,feature vector,situation assessment,case base reasoning |
Field | DocType | Citations |
Data mining,Computer science,Battlespace,Situation analysis,Artificial intelligence,Cluster analysis,Case-based reasoning,Distributed computing | Conference | 1 |
PageRank | References | Authors |
0.54 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carl G. Looney | 1 | 198 | 21.58 |
| Lily R. Liang | 2 | 143 | 11.40 |