Polarimetric HRRP Recognition Based on ConvLSTM With Self-Attention
Liang Zhang, Yang Li, Yanhua Wang, Junfu Wang, Teng Long
Abstract
Polarimetric high resolution range profile (HRRP) holds great potential for radar automatic target recognition (RATR) owing to its capability of providing both polarimetric and spatial scattering information. Conventional polarimetric HRRP recognition methods generally extract a set of features and put them into a well-trained classifier, which heavily rely on expertise. On the contrary, deep learning can automatically learn deep features of the training data and has obtained state-of-the-art results in many classification tasks. In this article, a novel deep model based on convolutional long short-term memory (ConvLSTM) network and self-attention mechanism is proposed for polarimetric HRRP recognition. In the proposed model, ConvLSTM employs the convolutional operator and recurrent LSTM structure to capture and aggregate target polarimetric and spatial scattering information from different dimensions simultaneously. Self-attention mechanism is introduced before ConvLSTM to make the model focus on the discriminative range cells and help improve the learning capacity of ConvLSTM. Experiment results on the simulated and measured datasets demonstrate the effectiveness of the proposed model for multi-class targets recognition. The results of two expanded experiments also show that this model performs well in noise and small training sample cases, showcasing good potentials in practical applications.