According to the characteristics of Doppler frequency shift of radar moving target in the fast time dimension, this paper proposes an intelligent detection and identification method of radar moving target based on YOLO(you only look once)v5s. First, the dataset is constructed based on the received target echo signal, and then the echo data dimension is placed and converted into images. Finally, the data is input into YOLOv5s for training, and the detection and classification of radar moving targets are realized by learning the characteristics of moving target Doppler frequency shift. The simulation experiment shows that the detection method can realize the effective detection and identification of radar motion and stationary targets.
The traditional Direction of Arrival (DOA) estimation algorithms are based on model parameters, which depends on the accuracy of the array model. When the array model has errors, the matching between the model and the data will fail, which affects the estimation performance to some extent. Therefore, this paper constructs the nonlinear relationship between the received signal and its spatial spectrum through the neural network framework, and uses the data-driven of deep learning to realize the DOA estimation. The neural network consists of an autoencoder network and multiple parallel 1-D VGG networks to achieve spatial spectrum estimation of the angle region. The simulation results show that the DOA estimation method proposed in this paper has good generalization ability, and also show good robustness under array error conditions.
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