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A Low Sidelobe Virtual Array Beamforming Optimization Method for Smart Devices
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AI
DOI：10.1109/tce.2026.3691312.png)
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Abstract
En 中文
Microphone array beamforming is a key technique for extracting desired speech signals from noisy environments in smart devices. To address the high sidelobe limitation in the beam pattern of virtual arrays, this paper proposes a low sidelobe virtual array beamforming (VAB) optimization method based on an improved dung beetle optimizer (IDBO). The IDBO integrates chaotic map, tournament selection, and Gaussian mutation to prevent premature convergence and avoid local optima. Simulation and experimental results demonstrate the effectiveness of the proposed method. The IDBO suppresses the maximum sidelobe level (MSLL) to below −20 dB over signal-to-noise ratio (SNR) ranging from −10 dB to 10 dB. Compared to state-of-the-art algorithms, the proposed method achieves an average MSLL reduction of approximately 0.5 dB while maintaining stable performance. Furthermore, it attains an output SNR of up to 10.8 dB, outperforming existing methods by 0.8–2.8 dB across various input SNR scenarios, demonstrating its superiority for VAB optimization in smart devices.
Keywords:
Microphone array beamforming
virtual array
dung beetle optimizer
low sidelobe level
smart device
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