Ground surveillance radar targets classification based on 2D multichannel adaptive weighted CNN

Meng Xu; Yibin Rui; Yaohui Wang; Renhong Xie; Peng Li; Mingliang Liao

doi:10.1117/12.3023245

28 March 2024 Ground surveillance radar targets classification based on 2D multichannel adaptive weighted CNN

Meng Xu, Yibin Rui, Yaohui Wang, Renhong Xie, Peng Li, Mingliang Liao

Proceedings Volume 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023); 130910X (2024) https://doi.org/10.1117/12.3023245
Event: Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 2023, Xi’an, China

Abstract

In this paper, a new 2D convolutional neural network (CNN) model is proposed for the classification of people, cars, and UAVs detected by low-resolution ground surveillance radars. Process the signals of people, cars, and UAVs in different domains to obtain the unique characteristics of the target signal, which can be used for target classification and recognition. Using the newly designed model to classify radar targets is divided into three steps. First, the Toeplitz matrix is used to reconstruct the 1D radar signal into 2D signals, and then a multi-channel adaptive attention module is constructed to classify radar targets. In the first step, since 2D radar signals are required for training, the 2D data is reconstructed using the Toeplitz matrix. In the second step, a channel attention module and a coordinate attention module weighted with adaptive coefficients are constructed, and the two are combined to form a multi-channel adaptive attention module. This module extracts features from the input through receptive fields of different sizes on different channels, and performs feature fusion. Since the overall network introduces a residual structure, the possible gradient disappearance/explosion problem in backpropagation is effectively solved. Tested on the actual human, car and UAV data sets, the accuracy rate reached 98.7% on the original time domain test set, 96.9% accuracy rate was achieved on the original frequency domain test set.

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

Citation Download Citation

Meng Xu, Yibin Rui, Yaohui Wang, Renhong Xie, Peng Li, and Mingliang Liao "Ground surveillance radar targets classification based on 2D multichannel adaptive weighted CNN", Proc. SPIE 13091, Fifteenth International Conference on Signal Processing Systems (ICSPS 2023), 130910X (28 March 2024); https://doi.org/10.1117/12.3023245

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