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

Summary: 1
Development and Performance Analysis of a Class of Intelligent Target
Recognition Algorithms
Mark Tillman
Defense Intelligence Agency
Missile and Space Intelligence Center
Redstone Arsenal, AL 35898-5500
rmt@msic.dia.mil
Payman Arabshahi
Department of Electrical and Computer Engineering
University of Alabama in Huntsville
Huntsville, AL 35899
payman@anahita.eb.uah.edu
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

  

Source: Arabshahi, Payman - Applied Physics Laboratory & Department of Electrical Engineering, University of Washington at Seattle

 

Collections: Engineering