Marine Radar Small Target Classification Based on Block-Whitened TimeFrequency Spectrogram and Pre-Trained CNN

This article presents a classification method to classify different marine floating small targets, which can realize effective classification of different targets in strong clutter background. The design of proposed classification method is primarily based on block-whitened time-frequency spectrogram and pre-trained convolution neural network (CNN). Block-whitening clutter suppression is used to process target echoes. By converting a strong clutter background to an approximately noisy background, the effect of strong clutter on classification is reduced. Then, the time-frequency spectrogram of targets is extracted from the block-whitened target echoes, which converts a signal in time domain into a time-frequency spectrogram with more information. In addition, the block-whitened time-frequency spectrograms are input to a pre-trained CNN for feature extraction and classification training. By exploiting pre-training procedure, the proposed method can effectively classify different marine floating small targets and solve the problem of limited target samples in practical applications. Finally, a dataset of three kinds of measured maritime radar targets is constructed to verify the effectiveness of proposed method. Experimental results show that compared with competitors, the pre-trained CNN with block-whitened time-frequency spectrograms can achieve higher performance on the measured dataset.

Automated summary

This article presents a method for effectively classifying different marine floating small targets in strong clutter background. It is based on block-whitened time-frequency spectrogram and pre-trained convolution neural network (CNN). The proposed method suppresses clutter and converts it to approximately noisy background, reducing its impact on classification. It extracts time-frequency spectrogram of targets from block-whitened echoes, provides more information. The pre-trained CNN using block-whitened time-frequency spectrograms achieves higher performance on the measured dataset compared to competitors.