Navigation jamming signal recognition based on long short-term memory neural networks
Dong Fu, Xiangjun Li, Weihua Mou, Ming Ma, Gang Ou
Abstract
This paper introduces the time-frequency analyzed long short-term memory (TF-LSTM) neural network method for jamming signal recognition over the Global Navigation Satellite System (GNSS) receiver. The method introduces the long short-term memory (LSTM) neural network into the recognition algorithm and combines the time-frequency (TF) analysis for signal preprocessing. Five kinds of navigation jamming signals including white Gaussian noise (WGN), pulse jamming, sweep jamming, audio jamming, and spread spectrum jamming are used as input for training and recognition. Since the signal parameters and quantity are unknown in the actual scenario, this work builds a data set containing multiple kinds and parameters jamming to train the TF-LSTM. The performance of this method is evaluated by simulations and experiments. The method has higher recognition accuracy and better robustness than the existing methods, such as LSTM and the convolutional neural network (CNN).