Abstract:

The acoustic spectrogram is a fundamental input for many underwater acoustic signal detectors. This presentation describes an effort to place theoretical bounds on the performance of these detectors and to provide insight into the structure of practical processors. The presentation begins with a description of the Neyman--Pearson optimal detector, the likelihood ratio test, and its use for signal detection in colored Gaussian noise scenarios. The presentation continues by developing successively more complex detection scenarios that necessitate the use of background estimation and, consequently, the generalized likelihood ratio test (GLRT). Parameter estimation from several common noise spectral estimation (NSE) techniques is included in the GLRT detection scenarios. In addition, the presentation develops guidelines for matching NSE parameter settings to signal characteristics. Then, the relationships among several relevant signal-to-noise ratios (SNRs) is developed and their correlation with detection performance is considered. Finally, the presentation considers departures from the Gaussian distribution assumption for background and signal processes and the resulting consequences. [sup a)]Ensign, USN