A Miniaturized Acoustic Metamaterial for Weak Signal Detection

Weak signal detection plays an essential role in industrial measurements and equipment monitoring. However, identifying weak target signals and mining the desired features by using traditional signal processing methods are cumbersome and difficult, where the performance and effect of these post-processing approaches always subjected to the component complexity and noise interference of the raw signal. To address this issue, inspired by the acoustic compression and acoustic rainbow trapping effects of acoustic metamaterials, this study designed a miniaturized gradient acoustic metamaterial (MGAM) to achieve an enhanced effect of weak signal detection. Different from the post-processing approaches, MGAM aims to detect and enhance the desired weak features in the process of front-end perception. Numerical simulations and experiments demonstrated that the interference signal outside the target frequency can be effectively suppressed, and the weak signal at the target frequency can be well recovered draw support from MGAM, even under the high signal-to-noise ratio condition. Additionally, the designed MGAM has a compact structure and can integrate intelligent sensors through embedding MEMS microphones, which provides a new idea for solving weak signal detection and realizing intelligent sensing at the acquisition side. These indicates that the proposed MGAM-based method is effective and convenient to detect weak signals with the desired features enhanced.