Oleg A. Godin

Problem Statement

Performance of passive sonars deployed on mobile platforms is limited by self-noise, which overshadows signals from quiet or distant targets.

This work adapts, verifies, and applies the machine-learning techniques, which have been recently developed for moored sonars, to separate the AUV self-noise from target signals and infer the AUV speed from flow noise. Importantly, the separation of flow noise from ambient and target sounds will be achieved in real time in single-hydrophone data.

Research will be performed in collaboration with USNA, where CDR Walters will return as a PMP after his PhD defense.  

Impact 

Exclusion of flow noise will allow (i) using AUVs for characterization of ambient sound with unprecedented spatial resolution, (ii) improved passive characterization of the seabed using drifting vertical arrays, and (iii) using AUVs for noise interferometry-based passive acoustic tomography and thermometry of the ocean.

Real-time retrieval of the AUV speed from flow noise will help to improve navigation of small AUVs as well as AUV stealthiness by eschewing the use of active sonar for speed measurements.

The anticipated warfighting impact of the flow noise exclusion includes increased detection range of passive sonar, improved accuracy of target localization, and higher speeds of operations with towed arrays.

Validity and accuracy of the AUV speed retrieval from flow noise will be verified against the measurements with a Doppler velocimeter made on the same AUV. Separation of flow noise and acoustic waves will be tested during local shipping events using AIS data for the ship information.

Transition

Results of the proposed research affect operators of all AUVs with hull-mounted hydrophones used for target detection, environmental sensing, or communications. The proposed research is also expected to have implications for operation of towed hydrophone arrays.

After proof of concept is achieved, reimbursable funds will be sought from ONR Code 321, Ocean Engineering and Marine Systems program for advanced technology development.