Abstract
The intra-pulse modulation of radar emitter signals is a key feature for analyzing radar systems. Traditional methods which require a tremendous amount of prior knowledge are insufficient to accurately classify the intra-pulse modulations. Recently, deep learning-based methods, especially convolutional neural networks (CNN), have been used in classification of intra-pulse modulation of radar emitter signals. However, those two-dimensional CNN-based methods, which require dimensional transformation of the original sampled signals in the stage of data preprocessing, are resource-consuming and poorly feasible. In order to solve these problems, we proposed a one-dimensional selective kernel convolutional neural network (1-D SKCNN) to accurately classify the intra-pulse modulation of radar emitter signals. Compared with other previous methods described in the literature, the data preprocessing of the proposed method merely includes zero-padding, fast Fourier transformation (FFT) and amplitude normalization, which is much faster and easier to achieve. The experimental results indicate that the proposed method has the advantages of faster speed in data preprocessing and higher accuracy in intra-pulse modulation classification of radar emitter signals.
Highlights
Intra-pulse modulation classification of radar emitter signals is a key technology, which helps to analyze the radar systems. It plays an important role in electronic support measure (ESM) systems, electronic intelligence (ELINT) systems and radar warning receivers (RWRs) [1,2,3]
As the length of sampled radar signals varies, it is hard to choose a suitable shape of time-frequency image (TFI) for convolutional neural networks (CNN)’s input to balance the speed for training and testing and classification accuracy, which leads to the poor feasibility
Considering these limitations and inspired by [18], which employed a multi-branch convolutional network and a dynamic selection mechanism in CNNs that allows each neuron to adaptively select its receptive field size based on multiple scales of input information, this paper proposed a 1-D selective kernel convolutional neural network (1-D SKCNN) for intra-pulse modulation classification of radar emitter signals
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. As the length of sampled radar signals varies, it is hard to choose a suitable shape of time-frequency image (TFI) for CNN’s input to balance the speed for training and testing and classification accuracy, which leads to the poor feasibility Considering these limitations and inspired by [18], which employed a multi-branch convolutional network and a dynamic selection mechanism in CNNs that allows each neuron to adaptively select its receptive field size based on multiple scales of input information, this paper proposed a 1-D selective kernel convolutional neural network (1-D SKCNN) for intra-pulse modulation classification of radar emitter signals. The results of the data preprocessing: the normalized frequency-domain sequences, will be used to train the proposed 1-D SKCNN This proposed method could classify eleven different intra-pulse modulations of radar emitter signals, which have a relatively wide interval for both duration and bandwidth.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
