Fully Convolutional Network-Based Fast UAV Detection in Pulse Doppler Radar

With the popularity of drones, how to conduct effective and fast detection of unmanned aerial vehicle (UAV) to prevent unauthorized flying becomes a hot topic. Based on statistical theory, traditional constant false alarm rate (CFAR) works well on data with uniform background. But for low-slow-small UAV, it is prone to miss detection. In recent years, data-driven deep learning method is proved to have better performance than CFAR. However, the use of sliding window to convert complex detection task into simple classification task leads to low efficiency. In this paper, we propose a fast detection method that applies a fully convolutional network on the whole range-Doppler map. To achieve comparable accuracy to our previous work, the network is firstly designed on the principle that the effective receptive field of unit in the feature map for prediction is close to the size of the sliding window. And the best bifurcation position of classification and regression is searched. Then, considering the imbalance of positive and negative samples, a new scheme to create GT data is designed to expand the positive samples, and random sampling of negative samples is adopted further. Lastly, a post processing mechanism combining probability thresholding and minimum deviation positioning is developed for accurate location of target. Comparison with existing methods on the experimental data shows that the proposed method can increase the detection speed by up to 47 times while maintain a promising accuracy.