Radar Signal Classification Based on Cascade of STFT, PCA and Naïve Bayes
Yuanyuan Guo, Xudong Zhang · 2016
In this paper, for radar signals with different ways of frequency or phase modulation, Cascade of Short Time Fourier Transform (STFT) and Principal Component Analysis (PCA) is employed for effective Feature Extraction. Compressive ratios of dimensions are quite impressive. For the same features, applying Naïve Bayes on the whole set of radar signals, higher accuracy rate is got than the case where classifier is SVM. Besides, a Hierarchical Structure is designed for finer classifications of radar signals. Comparison shows that Hierarchical Structure contributes little to increase of accuracy rate of radar signal classification by cascade systems on the whole set. Models are trained on 10db and tested on different Signal Noise Ratios (SNR).