Target recognition algorithm based on HRRP time-spectrogram feature and multi-scale asymmetric convolutional neural network

Abstract

A radar HRRP recognition algorithm based on time-spectrogram feature and multi-scale convolutional neural network is proposed to address the difficult feature extraction and low accuracy in space target recognition. Firstly, the normalization is used to eliminate the intensity sensitivity, the absolute alignment of multiple dominant scatterers is used to eliminate the translation sensitivity, and the radar Doppler velocity is used to eliminate the widening effect, distortion and wave crest splitting on HRRP caused by high-speed motion of the target. Then, the method applies the time-frequency analysis to the preprocessed HRRP to extract the time-frequency diagram. Finally, the time-frequency features are extracted with different scales of fineness and different directions through asymmetric convolution of different scales. The data processing results demonstrate that the present method has a high target recognition accuracy. In addition, the present improves the anti-posture sensitivity and target recognition on the same platform.