Development and Performance Analysis of a Class of Intelligent Target Recognition Algorithms

Abstract This paper develops and compares two fuzzy logic based and a traditional rule-based pattern recognition system, which perform target recognition with data from a typical range and doppler resolving radar. The parameters used by the pattern recognition systems are target altitude, velocity, range from nearest base, and radar cross section. The pattern recognition systems identify four classes of aircraft: fighter/interceptors, large bombers, rotary craft, and vertical take off and landing (VTOL) combat aircraft. The first fuzzy based pattern recognition method classifies targets by selecting the aircraft with the maximum summed amount of membership, giving a classification accuracy of 94% (average). The second approach classifies targets by selecting the aircraft through a max-min fuzzy decision system. This results in a 99% average accurate classification. The traditional rule-based method implements an expert system and correctly classifies 75% (average) of the targets.