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The Surveillance Test Bed (STB) is a program under development for the Strategic Defense Initiative Organization (SDIO). Its most salient features are (1) the integration of high fidelity backgrounds and optical signal processing models with algorithms for sensor tasking, bulk filtering, track/correlation and discrimination and (2) the integration of radar and optical estimates for track and discrimination. Backgrounds include induced environments such as nuclear events, fragments and debris, and natural environments, such as earth limb, zodiacal light, stars, sun and moon. At the highest level of fidelity, optical emulation hardware combines environmental information with threat information to produce detector samples for signal processing algorithms/hardware under test. Simulation of visible sensors and radars model measurement degradation due to the various environmental effects. The modeled threat is composed of multiple object classes. The number of discrimination classes are further increased by inclusion of fragments, debris and stars. High fidelity measurements will be used to drive bulk filtering algorithms that seek to reject fragments and debris and, in the case of optical sensors, stars. The output of the bulk filters will be used to drive track/correlation algorithms. Track algorithm output will include sequences of measurements that have been degraded by backgrounds, closely spaced objects (CSOs), signal processing errors, bulk filtering errors and miscorrelations; these measurements will be presented as input to the discrimination algorithms. The STB will implement baseline IR track file editing and IR and radar feature extraction and classification algorithms. The baseline will also include data fusion algorithms which will allow the combination of discrimination estimates from multiple sensors, including IR and radar; alternative discrimination algorithms may be substituted for the baseline after STB completion.
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