access icon free Optimal tone detection for optical fibre vector hydrophone

An optimal tone detector based on the Neyman–Pearson criterion is proposed for the optical fibre vector hydrophone (OFVH). The detector takes account of the difference between noise levels on the acoustic pressure channel and the three particle acceleration channels of an OFVH. Explicit expressions for the impulse responses of pre-filters that are central to the proposed detector are given. Analyses show that the proposed detector is equivalent to the minimum variance distortionless response beamformer for the OFVH. In the case of identical noise on all particle acceleration channels, the signal-to-noise ratio (SNR) gain of the detector is dB ( is the noise power ratio of OFVH channels at the tone frequency), whereas the SNR gain also depends on target direction and is bounded by and dB when noise on all particle acceleration channels are different. Results from both simulations and lake experiment data show that the proposed detector outperforms tone detectors that use (i) the acoustic pressure signal, (ii) the particle acceleration signals and (iii) equally the combination of acoustic pressure signal and particle acceleration signals.

Inspec keywords: fibre optic sensors; hydrophones; acoustic signal detection; acoustic signal processing

Other keywords: OFVH channels; SNR gain; identical noise; impulse responses; minimum variance distortionless response beamformer; optical fibre vector hydrophone; optimal tone detection; noise power ratio; noise levels; Neyman-Pearson criterion; acoustic pressure channel; signal-to-noise ratio gain; particle acceleration channels

Subjects: Acoustic signal processing; Signal detection; Oceanographic and hydrological techniques and equipment; Fibre optic sensors; fibre gyros; Fibre optic sensors; Transduction; devices for the generation and reproduction of sound; Sonic and ultrasonic transducers and sensors; Underwater sound

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-rsn.2018.5174
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content/journals/10.1049/iet-rsn.2018.5174
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