Radar Emitter Classification Based on a Multiperspective Collaborative Clustering Method and Radar Characteristic Spectrum

Algorithm 1

Processing steps of the multiperspective collaborative clustering algorithm.

Input: Perspective 1 basic signal characteristic data and perspective 2 intrapulse feature data

Output: Cluster label (data label for perspective 1 and data label for perspective 2)

Steps:

Step 1. Apply KPCA dimensionality reduction to the basic signal characteristic data of perspective 1 to obtain subspace , and apply KPCA dimensionality reduction to the intrapulse feature data of perspective 2 to obtain subspace .

Step 2. Sort subspaces and based on the nonuniform density-based spatial clustering of applications with noise (DBSCAN) algorithm, and obtain the respective cluster labels and of perspectives 1 and 2.

Step 3. Determine the similarity between and based on the Jaccard coefficient. The clustering process terminates if the similarity is greater than a preestablished threshold; otherwise, continue to the next step.

Step 4. Apply LDA(linear discriminant analysis) dimensionality reduction to perspective 2 data based on perspective 1 labels to obtain a new subspace , and apply LDA reduction to perspective 1 data based on perspective 2 labels to obtain a new subspace . Return to step 2.