A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis

Algorithm 1

Classification Algorithm 1.

	Input: The original signal vectors that need to be classified.
	The classification number .
	Output: The classified label vector .
	Compute by using self-adaptive filtering for ;
	Compute frequency spectrum of ;
	Compute center of each class by using WCM;
	Select training samples around and record their labels ;
	Create the PNN classifier by using and ;
	Determine the class of the remaining samples.
	End: Classification Algorithm 1.