Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis

The modulation classification of the non-cooperation underwater acoustic (UWA) communication signals is extremely challenging due to the adverse UWA channel transmission characteristics and low signal to noise ratio (SNR), which lead to considerable impairments of the signal features. In this paper, the principal component analysis (PCA) is proposed for efficient extraction of the power spectra and square spectrum features of UWA signals at the presence of multipath, Doppler, and noise induced in UWA channels. The employment of PCA enables extraction of the principal components associated with different modulation mode as the input vector of classier, thus reducing the feature dimension and suppressing the influence of UWA channels and environmental noise. With the features obtained by PCA, an artificial neural network (ANN) classifier is adopted for modulation recognition of UWA communication signals. The experimental modulation classification results obtained with field signals in 4 different underwater acoustic channels show that the proposed PCA based modulation recognition method outperforms the classifier using classic features in terms of classification performance and noise tolerance.