Xiong, J.; Pan, J.; Du, M. A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments. Remote Sens.2023, 15, 4083.
Xiong, J.; Pan, J.; Du, M. A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments. Remote Sens. 2023, 15, 4083.
Xiong, J.; Pan, J.; Du, M. A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments. Remote Sens.2023, 15, 4083.
Xiong, J.; Pan, J.; Du, M. A Cascade Network for Pattern Recognition Based on Radar Signal Characteristics in Noisy Environments. Remote Sens. 2023, 15, 4083.
Abstract
Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.
Keywords
signal analysis; mode recognition; noise coding; deep learning; attention mechanism
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
