Abstract
The separation and recognition of radar signals are crucial in a complex electromagnetic environment, especially multi-component radar signals. However, most existing algorithms can only recognize dual-component signals. An algorithm based on semantic segmentation is proposed to separate the signal in the time-frequency domain and classify multi-component radar signals. An improved Cohen class time-frequency distribution (CTFD) is used to represent the one-dimensional signals as time-frequency images (TFIs). A convolutional denoising autoencoder (CDAE) is established to filter the TFIs. Three semantic segmentation networks are used, a fully convolutional neural network (FCN-8s), U-Net, and DeepLab V3+. The method can separate and recognize signals simultaneously and is applied to aliased signals composed of 1-4 components. The simulation results show that the proposed method provides excellent performance for separating and recognizing multi-component signals. At a signal-to-noise ratio (SNR) of 0 dB, the accuracies of the aliased radar signals with 1-4 components are 100%, 100%, 96.67%, and 93.75%, respectively. The separation and recognition algorithm can be adapted to other signal modulation types.
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Hou, C., Fu, D., Hua, L. et al. The recognition of multi-components signals based on semantic segmentation. Wireless Netw 29, 147–160 (2023). https://doi.org/10.1007/s11276-022-03086-7
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DOI: https://doi.org/10.1007/s11276-022-03086-7