Algorithm fusion for automated sea mine detection and classification

Abstract

The fusion of multiple detection/classification algorithms is proving a very powerful approach for dramatically reducing false alarm rate, while still maintaining a high probability of detection and classification. This has been demonstrated in several Navy sea tests. The high-resolution sonar is one of the principal sensors used by the Navy to detect and classify sea mines in mine hunting operations. For such sonar systems, substantial effort has been devoted to the development of automated detection and classification (D/C) algorithms. These have been spurred by several factors including (1) aids for operators to reduce work overload, (2) more optimal use of all available data, and (3) the introduction of unmanned mine hunting systems. The environments where sea mines are typically laid (harbor areas, shipping lanes, and the littorals) give rise to many false alarms caused by natural, biologic, and man-made clutter. The objective of the automated D/C algorithms is to eliminate most of these false alarms while still maintaining a very high probability of mine detection and classification (PdPc). The benefits of fusing the outputs of multiple D/C algorithms have been studied. We refer to this as algorithm fusion. The results have been remarkable, including reliable robustness to new environments. Even though our experience has been gained in the area of sea mine detection and classification, the principles described herein are general and can be applied to fusion of any D/C problem (e.g., automated medical diagnosis or automatic target recognition for ballistic missile defense).