Abstract
Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: One is model-based using the Minimum Description Length (MDL) criterion and the other is based on competitive learning. Computational complexity is analyzed for each algorithm and then compared. Simulation results show the superior performance of the model-based algorithm over competitive learning in terms of better classification accuracy, flexibility, and stability.
Original language | English (US) |
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Pages (from-to) | 1185-1196 |
Number of pages | 12 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 27 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2005 |
Bibliographical note
Funding Information:This research was supported by a grant from the Defense R&D Canada, Ottawa, Ontario, Canada.
Keywords
- Cluster validation
- Clustering
- Competitive learning
- Computational complexity
- Emitter classification
- MDL criterion
- Online process