Doppler spectrum recognition of bird and drone based on one-dimensional deep neural network

Longqiu Feng; Jun Hu; Yue Zhang; Yinsong Zhao

doi:10.1117/12.2631859

14 April 2022 Doppler spectrum recognition of bird and drone based on one-dimensional deep neural network

Longqiu Feng, Jun Hu, Yue Zhang, Yinsong Zhao

Proceedings Volume 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021); 1217829 (2022) https://doi.org/10.1117/12.2631859
Event: International Conference on Signal Processing and Communication Technology (SPCT 2021), 2021, Tianjin, China

Abstract

Aiming at the problems of low accuracy of bird and drone about radar samples, lack of relevant data, a doppler spectrum recognition method of bird and drone based on one-dimensional deep neural network is proposed. First, take Fourier transform on measured radar echo to acquire the doppler specturm vector of the target, to construct doppler specturm dataset.Then based on the characteristics of the doppler spectrum of bird and drone, design the network structure for doppler spectrum vector of target. To reduce the influence of target flight direction and SNR on accuracy, speed up the training and feature enhancing, the first two layers of network add modulo layer and normalization layer. Then connect the improved one-dimensional ResNet18 to build the entire networks. By training the target doppler specturm samples to get and optimize the final model. Experimental results show that this method can achieve excellent results on bird and drone doppler dataset, with accuracy over 97%.

Citation Download Citation

Longqiu Feng, Jun Hu, Yue Zhang, and Yinsong Zhao "Doppler spectrum recognition of bird and drone based on one-dimensional deep neural network", Proc. SPIE 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021), 1217829 (14 April 2022); https://doi.org/10.1117/12.2631859

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