Radar Target Detection Algorithm Using Convolutional Neural Network to Process Graphically Expressed Range Time Series Signals
Yan Dai, Dan Liu, Qingrong Hu, Xiaoli Yu
Abstract
Under the condition of low signal-to-noise ratio, the target detection performance of radar decreases, which seriously affects the tracking and recognition for the long-range small targets. To solve it, this paper proposes a target detection algorithm using convolutional neural network to process graphically expressed range time series signals. First, the two-dimensional echo signal was processed graphically. Second, the graphical echo signal was detected by the improved convolutional neural network. The simulation results under the condition of low signal-to-noise ratio show that, compared with the multi-pulse accumulation detection method, the detection method based on convolutional neural network proposed in this paper has a higher target detection probability, which reflects the effectiveness of the method proposed in this paper.