No abstract

In this paper, we propose a classification method of pulsed radar signal attributes by applying machine learning to a convolutional neural network and a long short-term memory(CNN-LSTM) network when the radio frequency and pulse repetition interval of the signal changes with a long period. CNN has a function to extract data features, and LSTM shows good classification performance when sequential data are highly correlated. However, LSTM has a problem that performance is degraded when the length of the input data is long. In the proposed method, CNN is applied to extract compressed features from long-period data to reduce the length of inputs to LSTM. In simulation results, the proposed method shows more than 95% correct classification rate when the data drop rate is 10% for 182,516 total radar signal attribute combinations.