Journal Article

Automatic Modulation Recognition of Radiation Source Signals Based on Data Rearrangement and the 2D FFT

Yi, Guanghua, Huang, Dingkun, Yan, Xiaopeng, Hao, Xinhong, Liu, Yangtian

2023

Overview

It is a challenge for automatic modulation recognition (AMR) methods for radiation source signals to work in environments with low signal-to-noise ratios (SNRs). This paper proposes a modulation feature extraction method based on data rearrangement and the 2D fast Fourier transform (FFT) (DR2D), and a DenseNet feature extraction network with early fusion is constructed to recognize the extracted modulation features. First, the input signal is preprocessed by DR2D to obtain three types of joint frequency feature bins with multiple time scales. Second, the feature fusion operation is performed on the inputs of the different layers of the proposed network. Finally, feature recognition is completed in the subsequent layers. The theoretical analysis and simulation results show that DR2D is a fast and robust preprocessing method for extracting the features of modulated radiation source signals with less computational complexity. The proposed DenseNet feature extraction network with early fusion can identify the extracted modulation features with less spatial complexity than other types of convolutional neural networks (CNNs) and performs well in low-SNR environments.

• Publisher: MDPI AG
• Subject: Artificial neural networks / automatic detection / Automatic modulation recognition / automatic modulation recognition (AMR) / Complexity / Computer applications / data rearrangement and the 2D FFT (DR2D) / DenseNet feature extraction network with early fusion / environment / exhibitions / extracts / Fast Fourier transformations / Feature extraction / Feature recognition / Fourier transforms / Neural networks / Radiation / radiation source signal / Radiation sources / remote sensing / Signal to noise ratio / simulation models / Support vector machines / Theoretical analysis / working conditions