Roles and assessment methods for models of sensor data exploitation algorithms

Abstract

The modern battlespace is populated with a variety of sensors and sensing modalities. The design and tasking of a given sensor is therefore increasingly dependent on the performance of other sensors in the mix. The volume of sensor data is also forcing an increased reliance on sensor data exploitation and content analysis algorithms (e.g., detecting, labeling, and tracking objects). Effective development and use of interconnected and algorithmic (i.e., limited human role) sensing processes depends on sensor performance models (e.g., for offline optimization over design and employment options and for online sensor management and data fusion). Such models exist in varying forms and fidelities. This paper develops a framework for defining model roles and describes an assessment process for quantifying fidelity and related properties of models. A key element of the framework is the explicit treatment of the Operating Conditions (OCs - i.e., target, environment and sensor properties that affect exploitation performance) that are available for model development, testing data, and model users. The assessment methodology is a comparison of model and reference performance, but is made non-trivial by reference limitations (availability for OC distributions of interest) and differences in reference and model OC representations. A software design of the assessment process is also described. Future papers will report assessment results for specific models.