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20 September 1989 Modeling Realistic Target Acquisition Using Infrared Sensors In Multiple Observer Scenarios
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Abstract
To accurately measure the effectiveness of an infrared sensor for military or industrial purposes, the interaction between the human operator and the sensor must be considered. In particular, modeling the search process is crucial towards measuring the length of time and probability of success of target acquisition in a particular scenario. The CNVEO (Center for Night Vision and Electro-Optics) search model, used by the US armed forces and several allied coutries, is essentially a single-target, single-observer model. In this paper, we extend the model to the case where several observers are searching for the target under changing atmospheric conditions. The obscurant/atmosphere is characterized by an average transmission coefficient, a spatial variance, and a persistence time during which the obscurant/atmosphere is assumed to be fairly constant. Correlation between observers is determined by the degree that a particular level of obscurant for one observer implies the same level of obscurant for a physically separated second observer at the same time. Analytical solutions and computer simulations show that the degree of correlation between observers greatly influences the probability detection and the average time for detection; the greater the degree of correlation, the longer the average time for detection. In addition, radically different results are obtained depending on the persistence time of the obscurant. Methods to implement such a model in a battlefield simulation are described.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. R. Rotman and Marta L. Kowalczyk "Modeling Realistic Target Acquisition Using Infrared Sensors In Multiple Observer Scenarios", Proc. SPIE 1110, Imaging Infrared: Scene Simulation, Modeling, and Real Image Tracking, (20 September 1989); https://doi.org/10.1117/12.960753
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