For over 30 years, the U.S. Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC) has specialized in characterizing the performance of infrared (IR) imaging systems in the laboratory and field. In the late 90's, AMRDEC developed the Automated IR Sensor Test Facility (AISTF) which allowed efficient deployment testing of aviation and missile IR sensor systems. More recently, AMRDEC has tested many uncooled infrared (UCIR) sensor systems that have size, weight, power, and cost (SWAPC) benefits for certain fielded U.S. Army imaging systems. To compensate for relatively poor detector sensitivities, most UCIR systems operate with very fast focal ratio or F-number (f/#) optics. AMRDEC has recently found that measuring the Noise Equivalent Temperature Difference (NETD) with traditional techniques used with cooled infrared systems produce biased results when applied to systems with faster f/# values or obscurations. Additionally, in order to compare these camera cores or sensor systems to one another, it is imperative to scale the NETD values for f/#, focus distance, and waveband differences accurately. This paper will outline proper measurement techniques to report UCIR camera core and system-level NETD, as well as demonstrate methods to scale the metric for these differences.

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Computational imaging techniques can be used to extend the depth of field of imaging sensors such that the sensors become less expensive to build and athermalize with no loss to performance. Optical phase can be manipulated to create an image that is optimized for a detection and tracking algorithm as well as reconstructed digitally to form an image suitable for viewing. A typical low-cost sensor which is used for target detection and tracking may run an algorithm which requires different features and resolution from its imagery than would a system optimized for a human. This offers a unique opportunity to optimize both optics and image processing for a system which can maximize mission performance as well as minimize production cost. Simple computational techniques have not yet been successful in passive, low-signal environments due to noise issues. This study examines the use of a simple computational technique in an algorithmic application in which optimal reconstruction may occur with lower noise. This paper will describe the model, simulation, and prototype which resulted from a detailed and novel system design and modeling process. The goal of this effort is to accurately model the anticipated performance and to prove actual cost savings of a tracking sensor which employs computational imaging techniques.