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This paper describes development and testing of a program that provides a quantitative metric for the comparison of night vision fusion algorithms. The user enters into the Metric Program the names of a thennal file, a vision file and the corresponding fused image file. The program assigns a fusion rating to the algorithm based on the following four quantitative tests: information content (Ic), vision retention (vr), thermal retention (tr), and the bar test to detect black segments. In Ic the information content ofthe fused image is compared with a weighted sum of the vision and thermal images. In yr the number of faint lights that the fused image failed to incorporate is counted. In tr the number of pixels from the thennal file included in the fused image is determined. With some fusion algorithms if one of the sensors is blocked, a black segment appears in that area in the fused image, thus losing the information from the unblocked sensor. To test for this the Metric Program creates a thermal file with three horizontal black bars. The program then allows the user to call the executable file of the algorithm under test. Then the user is asked to examine the fused image. if three pitch-black horizontal bars appear on the image, the algorithm fails the test. While the bar test is invariant to the vision/thennal image pair used, the other tests are not. For this reason it is suggested that an algorithm should be tested with 5 or 6 different image pairs and a mean fusion rating calculated. The program is used to evaluate several different algorithms. Day vision fusion algorithms are also tested.

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and is responsible for program and curriculum development. Dr. McCullough has over 30 years' experience in engineering practice and education, including industrial experience at the Tennessee Valley Authority and the US Army Space and Missile Defense Command. Her research interests include Engineering Ethics, Image and Data Fusion, Automatic Target Recognition, Bioinformatics and issues of under-representation in STEM fields. She is a former member of the ABET Engineering Accreditation Commission, and is on the board of the ASEE Ethics Division and the Women in Engineering Division.

Future flight vehicles will require increased speed, precision, and survivability under a wider variety of flight conditions than current technology supports. The traditional role of flight control systems (FCS) in providing stability and command augmentations must be expanded to achieve enhanced maneuverability, combat effectiveness, and self repair. Recent advances in the development of fuzzy logic controllers and neural networks provide a unique opportunity for achieving highly integrated, reliable, robust, and adaptive flight control systems which enhance maneuverability, accommodate uncertainties, and simplify design and maintenance.In this paper, an innovative fuzzy control system architecture, namely the Fuzzy-CMAC (Fuzzy Cerebellar Model Arithmetic Computer) neural network, is evaluated for use in advanced missile control systems. This fuzzy-neuro architectur~, exploits a synergism between the CMAC neural network and fuzzy logic control. CMACS and fuzzy logic are similar in terms of input encoding and mapping structure. Different features of both models are compared and a Fuzzy CMAC model which combines preferred features of both models is then established. The fuzzy CMAC controller utilizes available human control experience in the initial construction of its knowledge base. Then the CMAC neural network further provides powerful self-learning capability and flexibility in achieving robust performance over an expanded flight envelope. It also provides fine tracking during critical maneuvers. The proposed Fuzzy CMAC flight control system can be further trained to offer failure isolation, self repair, and mission optimized control.