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WANG Zhaolong, ZHANG Xiaokuan, FENG Weike, et al. Moving target detection method based on intelligent multiclassification and transfer learning for missile-borne radars with sum-difference beams[J]. Journal of Radars, in press. doi: 10.12000/JR25089
Citation: WANG Zhaolong, ZHANG Xiaokuan, FENG Weike, et al. Moving target detection method based on intelligent multiclassification and transfer learning for missile-borne radars with sum-difference beams[J]. Journal of Radars, in press. doi: 10.12000/JR25089
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Moving Target Detection Method Based on Intelligent Multiclassification and Transfer Learning for Missile-borne Radars with Sum-difference Beams

DOI: 10.12000/JR25089 CSTR: 32380.14.JR25089

  • Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China

Funds:  Innovation Capability Support Program of Shaanxi (2025ZC-KJXX-81), Research Program Project of Youth Innovation Team of Shaanxi Provincial Education Department (24JP221), The Youth Innovation Team of Shaanxi Universities, National Natural Science Foundation of China (62201611, 62301597, 62301598)
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  • Corresponding author: FENG Weike, fengweike007@163.com
  • Received Date: 2025-05-12
  • Rev Recd Date: 2025-07-31
    • Available Online: 2025-08-04
    Abstract
  • Existing moving-target detection methods for missile-borne sum-difference beam radars require large amounts of training range cell data, yet still exhibit low detection performance. To address these challenges, this paper proposes a new detection method based on intelligent multiclassification and network parameter transfer learning. The proposed method uses a small set of training range cell data to construct a dataset for training a deep Convolutional Neural Network (CNN), which classifies data from the Range Cell Under Test (RCUT) into clutter (target-free) or target classes with different Doppler frequencies. To avoid the high computational cost and time associated with online training on measured data, an echo signal model is first established for moving target detection in the missile-borne sum-difference beam radar. This model is validated using measured data and subsequently used to generate simulated data for offline network training. In addition, to overcome common limitations of typical CNNs, such as large parameter sets, high computing complexity, and low training efficiency, this paper enhances the DenseNet architecture by incorporating a Feature Fusion Module (FFM) and a Spatial Attention Module (SAM), resulting in an improved FFM-SAM-DenseNet multiclassifier. Furthermore, conventional detection methods based on intelligent multiclassification require retraining the network when processing data from different RCUTs, leading to long convergence time and reduced efficiency. To solve this problem, transfer learning is introduced to share network parameters across multiclassifiers for different RCUTs, accelerating the overall convergence speed. Simulation and measured data show that, even with limited training range cell data, the proposed method achieves better moving target detection performance than existing typical methods.

     

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