Large-Scale Sparse Antenna Array Optimization for RCS Reduction With an AM-FCSN

• Published in: IEEE sensors journal Vol. 25; no. 3; pp. 5782 - 5794
• Main Authors: Ji, Lixia, Ren, Zhigang, Chen, Yiqiao, Zeng, Hao
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Antenna arrays; Antennas; Complexity; Deep learning; Feature extraction; Fully convolutional shortcut network based on an attention mechanism (AM-FCSN); hybrid-interval particle swarm optimization (HIPSO); large-scale sparse antenna array; Machine learning; Neural networks; Particle swarm optimization; Radar antennas; Radar arrays; radar cross section (RCS); Radar cross sections; Sensors; Switches
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3516038

The existing deep learning methods for radar cross section (RCS) reduction are unsuitable for large-scale sparse arrays because their large scales result in large numbers of classes and high network complexity levels. This article proposes a new deep learning method to solve these problems. First, a hybrid-interval particle swarm optimization (HIPSO) algorithm is presented. The number of classes is reduced using the presented HIPSO algorithm, which adaptively adjusts the sampling interval. Then, a fully convolutional shortcut network based on an attention mechanism (AM-FCSN) is designed. The optimal spatial arrangement is selected by the designed AM-FCSN. Finally, simulations show that the proposed HIPSO algorithm reduces the number of classes from <inline-formula> <tex-math notation="LaTeX">{O}\text {(} {{10}^{{4}}} \text {)} </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">{O}\text {(} {{10}^{{2}}} \text {)} </tex-math></inline-formula>. Moreover, compared with different neural networks, the proposed AM-FCSN achieves computational complexity and parameter complexity reductions of 38.46% and 80.2%, respectively, while attaining higher accuracy. The proposed method can effectively reduce the large-scale sparse array RCS in real time.