Radar High Resolution Range Profile Target Recognition Based On Temporal Dynamic Methods

A High-Resolution Range Profile(HRRP)is composed of the amplitude of the coherent summations of the complex returns from target scatterers in each range cell,which represents the projection of the complex returned echoes from the target scattering centers onto the radar Line-Of-Sight(LOS).Compared with the two-dimensional image(SAR image,ISAR image),HRRP data does not need a certain angle of the target relative to the radar,which is easier to obtain and process.With the gradual development of radar technology,the military requirement for HRRP target recognition technology is becoming more and more urgent.Therefore,this dissertation provide our researches for HRRP target recognition,which are supported by the Thousand Young Talent Program of China,NSFC(61771361),111Project(B18039),and the National Science Fund for Distinguished Young Scholars of China(61525105).This dissertation aims at proposing different machine learning algorithms to deal with the aspect sensitivity,time-shift sensitivity and temporal correlation of HRRP.The content of this paper mainly includes the following five parts:1.This chapter briefly reviews some basic theories of machine learning and defines the classification tasks.In addition,two classical neural network models,deep belief network(DBN)and stacked autoencoder(SAE),are introduced in detail.2.The traditional neural network methods only consider the envelope information of HRRP samples,ignoring the temporal correlation between the range cells of HRRP sample.In order to deal with this issue,a Recurrent Neural Network with voting Strategy(RNNvot)is proposed in this chapter,which converts the original HRRP data into a sequence and extracts discriminative features with the Recurrent Neural Network model.The output of the recurrent neural network is a sequence,while the HRRP sample corresponds to a single category.Therefore,we further combine the information at all timesteps with the voting strategy to predict the final label.3.There are two traditional methods to solve the time-shift sensitivity of HRRP.One is the alignment method,which aligns all samples with the template sample.However,this method requires a large amount of computation burden.Another is to extract translation invariant features,such as frequency domain features,which can effectively solve the time-shift sensitivity.However,this method changes the characteristics of data and loses the discriminative information.This chapter provides a Bidirectional Truncated Long-short Term Memory(BTLSTM).Specifically,the method firstly uses a truncation mechanism to extract the target area and extracts time-shift robust representation based on the target area.In order to consider the bidirectional temporal correlation,the model adopts the bidirectional LSTM to deal with the input representation and uses the voting strategy to predict the sample category.The experimental results based on measured data show that the current method is not only effective for recognition,but also very robust for time-shift sensitivity.4.In this chapter,we develop a Target-Aware Recurrent Attentional Network(TARAN)for Radar Automatic Target Recognition(RATR)based on High-Resolution Range Profile(HRRP)to make use of the temporal dependence and find the informative areas in HRRP,since it reflects the distribution of scatterers in target along the range dimension.Specifically,we utilize RNN to explore the sequential relationship between the range cells within a HRRP sample and employ the attention mechanism to weight up each timestep in the hidden state so as to discover the target area,which is more discriminative and informative.Effectiveness and efficiency are evaluated on the measured data.Compared with traditional methods,besides the competitive recognition performance,TARAN is also more robust to time-shift sensitivity thanks to the memory function of RNN and attention mechanism.Furthermore,detailed analysis of TARAN model are provided based on time domain and spectrogram features.5.In this chapter,a Gaussian Mixture Model Tensor Recurrent Neural Network(GMMTRNN)is proposed for Radar Automatic Target Recognition(RATR)based on High-Resolution Range Profile(HRRP),since it reflects the distribution of scatterers in target along the range dimension.Specifically,in order to consider the temporal correlation between the range cells within a HRRP sample,we transform the sample into sequential feature and utilize the RNN model to process it.Furthermore,based on the hypothesis that the correlation between range cells being different at different timesteps in the sequential feature,a Tensor Recurrent Neural Network(TRNN)model is designed to deal with the sequential feature,whose parameters are not shared at different timesteps,but depends on the input representation at each timestep.In order to solve the problem that the continuous HRRP sample being not suitable for determining the parameters,we employ the Gassian Mixture Model to cluster the input representations in sequential features and utilize the clustering category to decide the parameters at each timestep in TRNN.Experimental results based on measured data show that the GMM-TRNN achieves competitive recognition performance compared with traditional methods.