An interference signal recognition algorithm based on AutoEncoder-SVM model

Ningbo Xiao; Zuxun Song

doi:10.1117/12.3026130

8 April 2024 An interference signal recognition algorithm based on AutoEncoder-SVM model

Ningbo Xiao, Zuxun Song

Proceedings Volume 13090, International Conference on Computer Application and Information Security (ICCAIS 2023); 130902H (2024) https://doi.org/10.1117/12.3026130
Event: International Conference on Computer Application and Information Security (ICCAIS 2023), 2023, Wuhan, China

Abstract

For the typical interference signal identification problem, extracting the eigenvalues of the interference signal is a key step, and the number of eigenvalues has an important impact on the accuracy of classification and identification. Ensuring that the eigenvalues have good separation and stability under the condition of fewer eigenvalues is a difficult problem in the extraction of eigenvalues. Aiming at this problem, this paper proposes a dimensionality reduction and extraction algorithm of interference signal eigenvalues based on AutoEncoder. The algorithm uses AutoEncoder to reduce the dimension of multi-dimensional eigenvalues, and uses SVM to train and test the dimensionality-reduced data, and the experimental simulation comparison between PCA algorithm and AutoEncoder algorithm is carried out. The recognition rate of the dimension-reduced eigenvalues of the AutoEncoder algorithm is closer to the recognition accuracy of the unreduced multi-dimensional eigenvalues, however, compared with the PCA dimensionality reduction algorithm, the AutoEncoder dimensionality reduction algorithm has a higher accuracy and has obvious advantages.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ningbo Xiao and Zuxun Song "An interference signal recognition algorithm based on AutoEncoder-SVM model", Proc. SPIE 13090, International Conference on Computer Application and Information Security (ICCAIS 2023), 130902H (8 April 2024); https://doi.org/10.1117/12.3026130

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KEYWORDS

Principal component analysis

Detection and tracking algorithms

Signal to noise ratio

Dimension reduction

Feature extraction

Biomedical applications

Education and training

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