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5 May 2009 General framework for predicting environmental effects on signatures and sensor performance in complex environments
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Abstract
The performance and utility of battlefield and homeland security sensors depends on many complicated environmental and mission-related factors. This paper describes a general software design for predicting performance of such sensors. It is intended for application to a wide range of sensing modalities and based on an object-oriented framework that can be incorporated into Army command and control (C2) systems, decision support tools (DSTs), and force-on-force simulations. The approach breaks down sensor performance prediction into the following steps: (1) information gathering and construction of the tactical and environmental scenario, (2) translation of the scenario information, (3) target and noise signature prediction models, (4) prediction of sensor performance metrics, and (5) display of and interaction with the information. The main components for Steps 3 and 4 involve operations on signature features, which are described statistically by signal-model objects. These are the units of information needed by the sensor platforms for producing inferences such as the presence or location of a target. The features are generated by emitter platforms, propagated through the environment by feature transmitters (which use scenario translators, Step 2, to convert the atmospheric and terrain descriptions to the necessary model parameters), and then processed by the sensor platforms. To avoid generation and transmission of unneeded data, the architecture is based on a data "pull" (request) from the sensor platforms rather than the more commonly used approach in DSTs of data "push" from the emitter platforms.
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D. Keith Wilson and Richard Bates "General framework for predicting environmental effects on signatures and sensor performance in complex environments", Proc. SPIE 7333, Unattended Ground, Sea, and Air Sensor Technologies and Applications XI, 73330U (5 May 2009); doi: 10.1117/12.819497; https://doi.org/10.1117/12.819497
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