Wide-band Radar High Range Resolution Profile Target Recognition Based On Deep Neural Network

Radar automatic target recognition technology is mainly used for feature extraction and analysis of echoes captured by radar.Since the development of radar recognition technology,significant results have been achieved.Radar automatic target recognition technology can be divided into two categories based on traditional and deep neural network methods.Most of the traditional radar target recognition methods treat the collected radar signals as a whole,and do not take into account the timing correlation information inside of radar data.Although some traditional research methods have also proposed some algorithms to study the timing characteristics of radar data,they have only established shallow models to simulate them,and it is difficult to deeply explore the underlying timing correlation within radar data.Recurrent neural networks have achieved significant results in natural language processing since they were proposed.They can extract temporal correlation information between data.Compared with traditional methods,recurrent neural networks have also achieved good results in the field of radar high-resolution range profile(HRRP)recognition.The work of this paper is mainly divided into the following three points:1.Explore further of radar automatic target recognition technology,including radar automatic target recognition technology based on traditional methods and based on deep neural network,and further compare and analyze the recognition effect under traditional model and deep neural network model.2.Firstly,extract the spectral characteristics of the radar's original signal,and then use convolutional neural network to extract the radar signal's time-domain characteristics and spectral characteristics for classification and judgment.Then comparatively analyze the experimental results finally analyze the deficiencies of the convolutional neural network in extracting time domain features and derive a recurrent neural network.3.A deep neural network model based on time-frequency feature fusion based on Attention mechanism was proposed.The whole model uses the classical RNN model as the benchmark model,and the Attention mechanism is integrated on the basis of this structure.Secondly,the spectral characteristics of the radar time domain signal are extracted and the spectral features and time domain features are merged.The recurrent neural network performs the final identification.The recurrent neural network after the introduction of the Attention mechanism differs from the original recurrent neural network in that the implicit state of the last moment is used to make the final classification decision.It considers the hidden state information at each moment in the recurrent neural network,assigns different weights to the state of each moment,and summons the hidden layer features after assigning weights and finally uses the state after weighted summation.A final output that allows the network to focus on the most useful information for identification during training.The experiment proves that the recognition framework proposed in this paper has better recognition effect than the traditional radar target recognition method and the method based on convolutional neural network.