Numerous Department of Defense (DoD) sites contain unexploded ordnance (UXO) that are buried in sediments in shallow water areas. The remediation of these sites requires that UXO be detected and classified so that they can be removed. Munitions ranging from small projectiles to 2000 lb bombs must be detected and discriminated from non-hazardous items. The Naval Research Laboratory (NRL) has developed a prototype buried object detection sonar for other types of targets. The objective of this project is to adapt this existing technology to the detection and classification of buried UXO and demonstrate system performance against UXO. This project will develop and deliver optimized system characteristics, including system geometry and operating modes.

Technical Approach

The NRL buried object detection sonar uses a trio of novel parametric acoustic sources that are capable of producing a narrow transmit beam. This beam can be electronically steered back and forth as the sonar is towed above the seafloor. Parametric sonars achieve this narrow beam with small transducers by taking advantage of the non-linear stress-strain properties of water to mix two high frequency signals and produce a source signal at the difference frequency. This produces a high resolution 3D image of the sediments below the seafloor and objects embedded in them.  A combination of numerical prediction and laboratory tank experiments will be used to characterize and validate the sensor and develop a concept of operations.


The ability to accurately characterize buried objects as hazardous or non-hazardous without physically exposing them will result in enormous cost savings to the DoD. Even though some UXO may be buried deeply that remediation is not possible, their location needs to be documented and long-term observations conducted. Moreover, the scientific community will benefit from development of a sensor that promises high resolution images of sub-seafloor geology allowing exploration of other sediment processes. (Anticipated Project Completion – 2016)

  • Acoustic ,

  • Sensors