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A self-platforming electric field denoising algorithm for small speedboat
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AI
DOI：10.1016/j.measurement.2025.118908.png)
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Abstract
En 中文
• Proposes a self-platform denoising algorithm integrating modal decomposition (ICEEMDAN) with time–frequency transforms to address signal interference and detection failure caused by platform motion above 20 kn. • Achieves 89.7 % interference suppression at 25 kn, outperforming nine existing algorithms by 31.7 percentage points in signal-to-interference ratio. • Enables persistent electric field detection with 52.9 % target recognition continuity under high-speed conditions (25 kn), validated on scaled ship models. • Introduces motion-synchronized dynamic bandwidth allocation, overcoming the static platform assumption of traditional EMD-based methods. • Provides an engineering-viable solution for low-cost, high-speed maritime detection platforms without requiring auxiliary sensor arrays.
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