Under this project, Naval Surface Warfare Center Panama City Division worked towards resolving issues affecting sonar detection and classification/identification (C/ID) of underwater unexploded ordnance (UXO). Two main objectives were: (1) build a database of realistic sonar responses from UXO and clutter targets deployed in sand and mud underwater environments that could be used to develop and evaluate C/ID algorithms for separating UXO from bottom clutter, and (2) use this database to search for physics-based features capable of robust automated target recognition (ATR) performance.

Technical Approach

The primary effort towards building the UXO database involved providing logistical and technical support for three controlled sonar measurements from a linear rail deployed in the Gulf of Mexico off Panama City, Florida, in 2012-2013, and in St. Andrews Bay off Panama City, Florida, in 2014. These measurements leveraged Navy sponsored bottom reverberation and target scattering experiments led by the University of Washington Applied Physics Laboratory to collect sonar data from targets deployed on a sand and a mud ocean floor. A secondary effort to augment existing databases using finite element method and T-matrix modeling was also carried out. Both sets of data were processed to check sonar model simulations against more realistic data for UXO applications and to further the evaluation of backscatter phenomena for extraction of classification features. Classification analysis of this data was carried out by projecting target strength onto two primary spaces: frequency vs target aspect and frequency vs time. The frequency-aspect (acoustic color) representation was primarily used for targets detected at long ranges/shallow grazing angles, where aspect-dependent phenomena are most distinguishable and useful for carrying out template-matching studies. The time-frequency representation was used for high grazing angle data at aspects exhibiting strong backscatter such as broadside so that the viability of resonance-based features could be studied.


A database of target responses was assembled from the field experiments and from simulated data for targets deployed proud and buried in a sand and mud seafloor. These data represent responses of the target over a full 360o aspect range, a 5-30 kHz frequency band, and deployment ranges of 5-40 meters corresponding to grazing angles from 36o to 5o, respectively. The use of simulated data to augment target databases is a worthwhile alternative to costly data collection but accurate input parameters are needed. Input parameters such as target dimensions, casing material moduli, environmental layering, and environmental material parameters can vary enough that generic values can produce poor simulation matches with experiments. Template-matching studies were performed on the data processed into acoustic color plots. These studies were used to demonstrate discrimination trends under various conditions and show what parameters template-based discrimination is sensitive to. Template matching of data processed into time-frequency plots was also tried but with less success due to sensitivity to processing parameters. Algorithms to extract parameters associated with resonances in the frequency spectrum were devised and these parameters were used to form feature vectors. When fed to a clustering algorithm, these feature vectors appear to cluster difficult UXO targets reasonably well.


The results of this project will help further the development of ATR for underwater UXO by providing a database of UXO sonar responses for algorithm testing and training. Analyses provide classification benchmarks to guide future directions to pursue.

  • Physics-based ,

  • Automated Target Recognition (ATR) ,

  • Clutter ,

  • UXO Detection and Classification ,

  • Sonar