Transferred deep learning based waveform recognition for cognitive passive radar.

Highlights • A new two channel Convolutional Neural Network combining with Bi-directional Long Short-Term Memory achieves excellent performance for modulation recognition and protocol recognition. • Transfer learning is firstly introduced in waveform recognition filed to solve the model transferability problem across different recognition tasks, which can efficiently save the 60% training time and 50% training data. • A complete protocol signal dataset is provided for public research about waveform recognition in passive radar. Graphical abstract Abstract Passive radar capable of recognizing illumination of opportunities can improve the detection performance on account of its functional properties of environment adaptivity. Waveform recognition approaches based on Deep Learning can outperform traditional methods based on hand-crafted feature as shown in recent studies. In this paper, we propose a novel transferred deep learning waveform recognition method which makes use of multi-scale convolution and temporal dependency characteristics to improve the recognition performance. Firstly, we develop a two-channel convolutional neural networks combining with Bi-directional Long Short-Term Memory (TCNN-BL) architecture to extract features of different scales and merge past and future states. Then in order to solve the transferability problem across various sampling frequencies, we present a parameter transfer approach which initializes target domain classifier using source domain parameters. Based on our experiments on both public datasets and our own datasets, it can be demonstrated that the proposed approach significantly outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]