journal article·2026-04Open access

Low-noise and broad-bandwidth piezoelectric accelerometers for underwater acoustic vector sensor

Ze Fang, Wenlong Wang, Shuai Yang, Jie Jiao, Shuhao Wang, Yang Liu, Sheng Hong, Zhen Liu, Lisha Liu, Fei Li, Yaojin Wang, Yixin Yang

Abstract

The detection and localization of underwater targets is of great significance in seabed resource exploration, seabed topography drawing, underwater archaeology, and undersea rescues. The indispensable vector hydrophone, equipped with an integrated accelerometer, can detect underwater targets by combining pressure and vibration velocity measurements. Existing vector hydrophone technologies exhibit limitations in sensitivity, bandwidth, and noise floor—particularly for low-frequency acoustic signal detection. Herein, an equivalent noise model has been established, associated with the intrinsic piezoelectric multi-parameters, to guide vector sensor design. In turn, a low-noise and broad-bandwidth accelerometer was designed based on textured piezoelectric ceramics with high piezoelectricity ( d 33 ∼ 1258 pC/N) and low dielectric loss (tanδ ∼ 0.5%). The piezoelectric vector accelerometer exhibits a higher sensitivity of 1713 pC/g, a broad bandwidth of 1310 Hz (±3 dB), and a lower noise floor of 16.8 ng/Hz 1/2 @100 Hz, greatly outperforming commercial PZT-based accelerometers or state-of-the-art low-noise accelerometers. Furthermore, a vector hydrophone was constructed by orthogonally integrating three of the textured accelerometers, achieving a sensitivity of –183 dB and a self-noise floor of 51.9 dB at 100 Hz, which is significantly lower than that of the Knudsen SS0. The remarkable performance offers a technical strategy with great potential for use in active seismic, marine biology, and underwater acoustics monitoring, further advancing the application of textured sensors in these fields.
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Research context

OpenAlex topics: Innovative Energy Harvesting Technologies · Geophysics and Sensor Technology · Acoustic Wave Resonator Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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