title: Online clustering algorithms for radar emitter classification.
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.
authors.affiliation: TechnoCom Corporation, 16133 Ventura Blvd., Suite 640, Encino, CA 91436, USA. jliu@technocom-wireless.com
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Jim P Y
authors.initials: JP
authors.lastname: Lee
authors.lastname: Senior
authors.forename: Lingjie
authors.initials: L
authors.lastname: Li
authors.forename: Zhi-Quan
authors.initials: ZQ
authors.lastname: Luo
authors.forename: K Max
authors.initials: KM
authors.lastname: Wong
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D003199:Computer Systems; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D011825:Radar; D012815:Signal Processing, Computer-Assisted