Radar Target Detection Algorithm Using Convolutional Neural Network to Process Graphically Expressed Range Time Series Signals

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.

Automated summary

This paper proposes a target detection algorithm using convolutional neural network under low signal-to-noise ratio, which processes graphically expressed range time series signals. The algorithm first processes the two-dimensional echo signal graphically, and then detects the graphical echo signal using the improved convolutional neural network. Simulation results show that the proposed method has a higher target detection probability compared to the multi-pulse accumulation detection method, indicating its effectiveness.