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  • 标题:A Novel Feature-Based Detector for Underwater Acoustic Beacon Signals Using Superimposed Envelope Spectrum of Multi-Pulses
  • 本地全文:下载
  • 作者:Shuai Yao ; Yinjia Liu
  • 期刊名称:Journal of Marine Science and Engineering
  • 电子版ISSN:2077-1312
  • 出版年度:2021
  • 卷号:9
  • 期号:12
  • 页码:1337
  • DOI:10.3390/jmse9121337
  • 语种:English
  • 出版社:MDPI AG
  • 摘要:For tackling the challenge of in-time searching a sea-crashed plane, it is critical to develop a convenient and reliable detector for the underwater beacon signal. In the application of signal detection, a conventional detector such as linear correlation (LC) is used based on the assumption of Gaussian white noise, but it has turned out to be a poor choice in a sophisticated underwater environment. To address this issue, a novel feature-based detector using superimposed envelope spectrum (SES) of multi-pulses is proposed in this paper. The proposed detector firstly extracts the envelopes of the received multi-pulse signals and superimposes the envelopes according to the known period. Then, the harmonic features of the SES are derived and utilized in the feature judgment to make the final decision. The proposed method is evaluated together with several existing state-of-the-art detectors, including the matched filter (MF), the generalized likelihood ratio test (GRLT) detector, and the periodogram of the directly dislocation superposition (PDDS) detectors with constant false alarm probability. Compared with the conventional detectors, it is found that the proposed SES detector is more robust against the colored noise, the random phase, and the channel distortions caused by the sophisticated underwater environment. Simulation results show that, given a detection probability value of 90% and a false alarm probability value of 1%, the proposed detector shows a gain of 3–12 dB compared with the best one of the MF, GRLT, and the PDDS detectors under distorted channels in terms of signal-to-noise ratio (SNR) requirements, respectively. Experimental results based on lake trial data have also verified the validity and feasibility of the proposed feature-based detector.
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