HRRP target recognition method based on attentional depth bidirectional loop neural network

• Main Authors: YU YANZHEN, LIU AILIN, LI XUNGEN, LI ZIXUAN, PAN MIAN, ZHANG ZHAN, LYU SHUAISHUAI
• Format: Patent
• Language: Chinese; English
• Published: 15.01.2019
• Subjects: CALCULATING; COMPUTING; COUNTING; HANDLING RECORD CARRIERS; PHYSICS; PRESENTATION OF DATA; RECOGNITION OF DATA; RECORD CARRIERS

The invention discloses a HRRP target recognition method based on attentional depth bidirectional circulating neural network, firstly, the time-domain features of the data are extracted and the extracted time-domain features are segmented and non-uniformly quantized to obtain the coding of the local structure, and then the co-occurrence matrix is obtained by using the relationship between the local structure and several surrounding local structures, and then the structural embedding characteristics of the data are obtained by the co-occurrence matrix, and then the extracted embedded features are sent to a deep neural network composed of a full connection layer and a bi-directional loop neural network based on attention LSTM for training, at the same time, according to the output of the hidden layer of the loop network, the softmax layer is used to obtain the weight parameters of the attention model. Finally, the HRRP is identified by the softmax layer and the weight of the attention model, and the recognition