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

Journal: Sensors (Basel, Switzerland)
PMID: 36146219

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.

Authors

  • Yan Dai
    Laboratory of Veterinary Drug Development and Evaluation, College of Animal Science and Technology, Henan University of Science and Technology, Luoyang 471023, China.
  • Dan Liu
    Department of Bioengineering, Temple University, Philadelphia, PA, United States.
  • Qingrong Hu
    Defense Technology Academy of China Aerospace Science and Industry Corporation Limited, Beijing 100070, China.
  • Xiaoli Yu
    Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, PR China.

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