Liu Jun, Lee Jim P Y, Li Lingjie, Luo Zhi-Quan, Wong K Max
TechnoCom Corporation, 16133 Ventura Blvd., Suite 640, Encino, CA 91436, USA.
IEEE Trans Pattern Anal Mach Intell. 2005 Aug;27(8):1185-96. doi: 10.1109/TPAMI.2005.166.
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.
雷达辐射源分类是数据聚类的一种特殊应用，用于从接收到的雷达脉冲样本中对未知雷达辐射源进行分类。这项任务的主要挑战在于雷达脉冲样本的高维度、样本组规模小以及雷达脉冲簇位置相近。本文针对雷达辐射源分类开发了两种新的在线聚类算法：一种是基于最小描述长度（MDL）准则的基于模型的算法，另一种是基于竞争学习的算法。分析了每种算法的计算复杂度并进行了比较。仿真结果表明，基于模型的算法在分类准确率、灵活性和稳定性方面优于竞争学习算法。
