Abstract
The problem of detecting a signal of known form in a noisy message is a long-studied problem. In this paper, we formulate it as the test of a sharp hypothesis, and propose the Full Bayesian significance test of Pereira and Stern as the tool for the job. We study the FBST in the signal detection problem using simulated data, and also using data from OceanPod, a hydrophone designed and operated by the Dynamics and Instrumentation Laboratory at EP-USP.
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Notes
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This was possible since touristic boats are allowed in the park for diving visits, and we happen to know that during weekends they are likely to be near the park.
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Hubert, P., Stern, J.M., Padovese, L. (2018). Full Bayesian Approach for Signal Detection with An Application to Boat Detection on Underwater Soundscape Data. In: Polpo, A., Stern, J., Louzada, F., Izbicki, R., Takada, H. (eds) Bayesian Inference and Maximum Entropy Methods in Science and Engineering. maxent 2017. Springer Proceedings in Mathematics & Statistics, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-91143-4_19
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DOI: https://doi.org/10.1007/978-3-319-91143-4_19
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