Full Bayesian Approach for Signal Detection with An Application to Boat Detection on Underwater Soundscape Data

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.