国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

In recent years, the emergence of a variety of small smart detection devices（such as UAV, small smart car, etc.）has brought great challenges to traditional radar target recognition methods. The signal echo energy obtained when using radar to detect such small targets is usually low，which makes it difficult to identify them by using the traditional constant false alarm rate (CFAR) target detection method under the influence of complex environmental noise and clutter. In response to the above problems，this paper combines the deep learning method to propose a multi-class radar target recognition model based on residual connected Long Short-Term Memory (LSTM)，which takes the echo sequence data at adjacent time points at the same distance gate as the sample to design the data set，the multi-layer LSTM network is used to extract the timing information in the radar echo samples，and the residual connection is added to the network to avoid the problem of network degradation due to the increase of network layers. At the same time，the CCE (categorical cross entropy) function used for the multi-class classification problem is used as the loss function of the network to train the network and realize the recognition and classification of four types of targets, including UAV，smart car，pedestrian and noise. The experimental results show that the multi class radar target recognition model based on residual connected LSTM network has higher recognition accuracy and F1 value than the traditional CFAR detection method.