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

Sensors (Basel). 2022 Sep 11;22(18):6868. doi: 10.3390/s22186868.

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.

Keywords: convolutional neural network; detection probability; graphical; low signal-to-noise ratio.

MeSH terms

  • Algorithms
  • Neural Networks, Computer*
  • Radar*
  • Signal-To-Noise Ratio
  • Time Factors

Grants and funding

This research received no external funding.