Low-resolution radar target classification algorithm based on one-dimensional densely connected network

Meibin Qi; Kan Wang

doi:10.1117/12.2682379

20 June 2023 Low-resolution radar target classification algorithm based on one-dimensional densely connected network

Meibin Qi, Kan Wang

Proceedings Volume 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023); 127150L (2023) https://doi.org/10.1117/12.2682379
Event: Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 2023, Dalian, China

Abstract

To address the problem of low accuracy of traditional low-resolution radar target classification and recognition. In this paper, a low-resolution radar target classification algorithm based on a one-dimensional Densely Connected Convolutional Network (DenseNet) is proposed. The algorithm first directly downscales the Densely Connected Convolutional Network, then takes the original 1D radar target signal as the input for training, uses a segmented loss function for the characteristics of different classes of signals, makes the network use different loss functions in different training stages, and then back-propagates the loss to optimize the weights to improve the recognition effect of the network. The experimental results show that the recognition rate of the proposed method is higher than that of traditional radar target classification methods and simple one-dimensional convolutional neural networks (CNN) for low-spectral radar target classification, especially under low signal-to-noise ratio conditions, which fully demonstrates the effectiveness of the proposed method.

Citation Download Citation

Meibin Qi and Kan Wang "Low-resolution radar target classification algorithm based on one-dimensional densely connected network", Proc. SPIE 12715, Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023), 127150L (20 June 2023); https://doi.org/10.1117/12.2682379

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