Many active and former military installations have ordnance ranges and training areas with adjacent water environments in which unexploded ordnance (UXO) are present due to wartime activities, dumping, and accidents. These contaminated areas include coastal and inland waters both in the United States and abroad. Over time, such geographic areas are becoming less and less remote as the adjacent lands become further developed, and the potential hazard to the public from encounters with such UXO has begun to rise. Presently there exists no sufficiently effective capability to survey such underwater areas and map UXO locations.

The objective of this project was to explore the feasibility and advantages of applying the structural acoustic feature-based technique to the detection and identification of underwater UXO, especially for buried targets. In the structural acoustic regime, the echoes are related to the vibrational dynamics of the object, and time-frequency features in the scattered echoes can be used to “fingerprint” and identify the target.

Researchers made comprehensive state-of-the-art UXO scattering measurements in the Naval Research Laboratory structural acoustic underwater laboratory free-field and sediment facilities and off the coast of Panama City, Florida, using a rail-based robotic sonar system. Time- and frequency-based numerical models were applied.

The project focused on the long range mono-static scenario, and the research established the following: (1) Typical proud or partially buried UXO have sufficiently high target strength levels over the structural acoustic frequency band to be detectable out to modest ranges. (2) Relevance vector machine (RVM) identification algorithms trained on data in this band allow one to distinguish between UXO and non-UXO and to also distinguish between various UXO themselves. (3) RVM training data will have to include echoes for various burial pitch angles in addition to in-plane aspect angles. (4) Multi-path acoustic propagation significantly alters the frequency-angle features; however, a realistic propagation model can be used to include this complication in the operation of the RVM identification process.

A modest portion of the project’s efforts addressed the long range bi-static scenario, in particular forward and near-forward scattering. The research established the following: (1) UXO forward scattered echo levels remain higher than typical backscattered levels for all source-to-target aspect angles. (2) Unlike backscattering, forward scattering remains strong for typical UXO targets as the target becomes buried in the sediment. (3) A properly designed wave-number filter can be used to extract the forward echo from the overlapping and much stronger incident source signal.

Some effort also addressed in a preliminary fashion issues associated with the short-range, down-looking case, and the following results were achieved: (1) A strategy was developed for processing the acoustic color feature space associated with this 2-D measurement plane. (2) 2-D echo data bases were collected on a UXO target and two false targets buried 10 cm beneath the sediment surface. Preliminary RVM identification algorithms, trained on this data base as well as on comparable data bases generated by a finite element-based structural acoustics model, demonstrated good target feature separation. (3) Simple time-based images were successfully generated on the measured data and exhibited some promise for image-based data calls.

The structural acoustic approach to target detection and identification offers significant advantages over more conventional acoustic approaches that rely only on the formation of high resolution images. These advantages include a diverse set of “fingerprints” leading to low false alarm rates, longer range operation leading to wide area coverage, and low frequency sediment penetration leading to buried target prosecution.