This proposal addresses the problem of resolving ambiguous and unknown classification of emitter signals in submarine ESM systems. Attempts to reduce ambiguities by careful tailoring of the parameter library involve time-consuming analysis and have not generally been successful because the mix of potentially ambiguous signals is dependent on the scenario. Attempts to reduce the number of unknown signals through techniques such as altering parameter limits have the undesirable side effect of also increasing ambiguous identifications. The use of fuzzy logic has the potential to improve marginally ambiguous and unknown emitter classification problems without other adverse effects. A fuzzy logic-based classifier will produce decisions that correspond to the way a human would solve the same problem. The use of vague concepts typical of human descriptions of problems can be used in a fuzzy classifier to produce a powerful classification system that takes advantage of existing emitter parameter data. The fuzzy classifier will interface to existing submarine ESM systems. The three areas of study that will be addressed are: 1) analyze present ESM classification failures, 2) identify fuzzy logic algorithms and software, and 3) define breadboard classification software system.
