SBIR/STTR Award attributes
Radiological and nuclear incidents threaten the health and safety of US. To prepare for the occurrence of these incidents, the Defense Threat Reduction Agency (DTRA) evaluates both commercial and non-commercial systems expected to help Warfighters either prevent the occurrence of a radiological incident or mitigate the damage of a radiological incident that has already occurred. Existing simulator-based methods to evaluate radioactive isotope identification devices (RIIDs) enable users to calculate what a radiation detector will display given the detector’s hardware specifications. What these simulation solutions do not do however, is replicate the experience of using these detectors. As a consequence, when testing RIIDs using these solutions, evaluators are unable to assess the usability of the detectors under dynamic environmental conditions, how well Warfighters are able to integrate potential maintenance requirements (e.g., recalibration and device cooling) for the detector into their workflow, and device usability when supporting multi-person operations. The only way to test these factors, is to setup a real-world environment for device testing. This is a highly controlled test scenario using the actual detectors being assessed and radiological sources. This method enables testing of detectors in a representative real-world environment where the Warfighter’s usage of the device can be monitored. However, real-world testing has significant constraints, is difficult to control (weather and temperature) and is prohibitively expensive, limiting the replicability and scope of testing that can be performed. To provide a low-cost T&E methodology capable of not only supporting system evaluation, but also behavioral, human-factors, and workflow analytics, Aptima will build the CLEAR: RAD Suite, which is a real-time RIID test and evaluation (T&E) solution based on Aptima’s CLEAR modular mixed-reality T&E system. CLEAR: RAD Suite will enable evaluators to rapidly generate synthetic replicas (virtual twins) of real-world RIIDs and evaluate their usage within highly configurable virtual environments supported by a built-in analytics capability that will support data recording, pre-processing, and assessment.
