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2025-07-17
Research on Random Phase Feeding Optimization and Sidelobe Suppression in Phased Arrays Based on Dynamic SFLA
By
Progress In Electromagnetics Research C, Vol. 157, 207-213, 2025
Abstract
To address the challenge of balancing sidelobe suppression and computational efficiency in phased array random phase feeding optimization, this paper proposes a multi-objective collaborative optimization scheme based on the Dynamic Shuffled Frog Leaping Algorithm (DSFLA). By establishing a hardware-compatible binary encoding model for phase quantization errors and introducing sidelobe variance constraints, the method achieves joint optimization of peak sidelobe level (PSLL) and beam pattern flatness. Simulation results demonstrate: For 32-element Taylor-weighted arrays, optimized PSLL reaches -28.6 dB (8.8 dB improvement vs. initial) with sidelobe variance reduced from 3.5 dB² to 1.2 dB²; For Chebyshev-weighted arrays, PSLL achieves -31.2 dB. The algorithm maintains robust performance under practical imperfections including element spacing perturbations (0.02λ RMS error) where PSLL stabilizes at -27.3 dB (σ=0.9 dB), and phase quantization errors (5° RMS) yielding -27.9 dB PSLL. DSFLA significantly outperforms conventional methods - reducing convergence generations from 276 to 28 and computation time by 29.2% (85 s) versus ant colony optimization while demonstrating O(N1.5) scalability to 128-element arrays (PSLL=-32.1 dB in 218 sec). Real-time operation is feasible with PSLL=-27 dB achievable in ≤40 ms, meeting 50 ms radar beam-switching deadlines. This approach provides a practical solution for real-time beam control in high-precision phased array radar systems.
Citation
Li Wang, and Qiusheng Li, "Research on Random Phase Feeding Optimization and Sidelobe Suppression in Phased Arrays Based on Dynamic SFLA," Progress In Electromagnetics Research C, Vol. 157, 207-213, 2025.
doi:10.2528/PIERC25052601
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