Given the vast amount of image intelligence utilized in support of planning and executing military
operations, a passive automated image processing capability for target identification is urgently required.
Furthermore, transmitting large image streams from remote locations would quickly use available band
width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the
problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive
Resonance Theory approach to cluster templates of target buildings. The results show that the network
successfully classifies targets from non-targets in a virtual test bed environment.
A culturally diverse group of people are now participating in military multinational coalition operations (e.g., combined
air operations center, training exercises such as Red Flag at Nellis AFB, NATO AWACS), as well as in extreme
environments. Human biases and routines, capabilities, and limitations strongly influence overall system performance;
whether during operations or simulations using models of humans. Many missions and environments challenge human
capabilities (e.g., combat stress, waiting, fatigue from long duty hours or tour of duty). This paper presents a team
selection algorithm based on an evolutionary algorithm. The main difference between this and the standard EA is that a
new form of objective function is used that incorporates the beliefs and uncertainties of the data. Preliminary results
show that this selection algorithm will be very beneficial for very large data sets with multiple constraints and
uncertainties. This algorithm will be utilized in a military unit selection tool.