{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

Sinusoid Det JASA 1980

Sinusoid Det JASA 1980 - Detection of sinusoids in ocean...

Info icon This preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Detection of sinusoids in ocean acoustic background noise W. S. Hodgkiss and V. C. Anderson University of California at San Diego, Marine Physical Laboratory, Scripps Institution of Oceanography, San Diego, California 92152 (Received 19 January 1979; accepted for publication 26 September 1979) A common receiver structure for the detection of sinusoidal signals obscured by a noise background is the filter, square, and integrate processor. For this processor, an interpretation of postdetection integration gain is given in terms of a low-pass faltering of the noise power time series. Although a processing gain of 1.5 dB per integration intervaldoubling is predicted, subsequent analysis on the highly colored envelope spectrum of ambient ocean acoustic noise data in the kilohertz region shows that its nonstationarity significantly alters realizable processing gains for integration intervals of tens to hundreds of seconds. PACS numbers: 43.60.Gk, 43.30.Sf, 92.10.Vz, 43.30.Ng INTRODUCTION A common approach to the problem of detecting sinus- oids buried in an ambient ocean noise background has been to perform short-term coherent spectral analysis via FFT techniques, then incoherently average these power spectra over several consecutive blocks of data. Total incoherent integration intervals on the order of tens to hundreds of seconds are frequently employed. Performance prediction analyses typically associate a 1.5 dB "processing gain" for every doubling of this post- detection integration interval. Implicit in such predic- tions is stationarity of the ambient noise background. Recent experiments with a high gain, medium fre- quency, acoustic array have demonstrated significant noise nonstationarity at the output of extremely narrow beams for periods greater than 15 s. ' Of interest here will be the implications of such nonstationarity on pro- cessor performance in terms of realizable processing gain for various integration intervals. The organization of this paper is as follows: Sec. I defines the filter, square, and integrate processor in the context of FFT-based digital signal processors. A detectability measure is introduced and is intuitively related to the comparison of a given frequency domain "bin" with the average level of adjacent bins. Then, Sec. II provides an interpretation of postdetection inte- gration gain in terms of a low-pass filtering or smooth- ing of the noise power time series. The envelope spec- trum is introduced as a characterization of the noise power fluctuation about its mean value. Sec. III follows with an example of ambient ocean noise and the impli- cations its nonstationarity has on realizable processing gain. Lastly, a summary is provided in Sec. IV. I. SINUSOlD DETECTION VIA SPECTRAL ANALYSIS FFT-based digital signal processors are commonly used for the detection of sinusoidal signals obscured by a noise background. The discrete Fourier trans- form, the power spectrum, and the averaged power spectrum are each the optimal processor (in the sense of making a least-risk decision) for a particular signal
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern