Method for functional state recognition of multifunction radars based on recurrent neural networks

[1]  G Latombe,et al.  Fast Learning of Grammar Production Probabilities in Radar Electronic Support , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Herbert Jaeger,et al.  Observable Operator Models for Discrete Stochastic Time Series , 2000, Neural Computation.

[3]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[4]  Udit Satija,et al.  Specific Emitter Identification Based on Variational Mode Decomposition and Spectral Features in Single Hop and Relaying Scenarios , 2019, IEEE Transactions on Information Forensics and Security.

[5]  Xinwei Zheng,et al.  Radar emitter classification for large data set based on weighted-xgboost , 2017 .

[6]  Alex Wang,et al.  Syntactic Modeling and Signal Processing of Multifunction Radars: A Stochastic Context-Free Grammar Approach , 2007, Proceedings of the IEEE.

[7]  Feng Zhao,et al.  Method for operating mode identification of multi-function radars based on predictive state representations , 2017 .

[8]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[9]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[10]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[11]  Ali Mohammad-Djafari,et al.  Radar emitters classification and clustering with a scale mixture of normal distributions , 2019 .

[12]  Eric Granger,et al.  Graphical EM for on-line learning of grammatical probabilities in radar Electronic Support , 2012, Appl. Soft Comput..

[13]  Jin Liu,et al.  Novel Approach for the Recognition and Prediction of Multi-Function Radar Behaviours Based on Predictive State Representations , 2017, Sensors.