CRANFOA-22-0001

Description

Naval Surface Warfare Center, Crane Division is interested in receiving proposals for the following Basic Research Opportunity Areas: a. High Fidelity Radio Frequency Scene Generation for Real-Time Processing NSWC Crane seeks novel approaches to the problem of generating complex radio frequency (RF) scenes for use in real-time hardware-in-the-loop (HWIL) simulations. Of particular interest are synthetic RF scenes for navigation purposes such as terrain feature mapping radars, Doppler mapping radars, and object identification purposes within radar scenes generated by pulse-doppler radars and synthetic aperture radars. RF scenes generated by radars such as LIDAR are of interest as well. For HWIL applications, an RF scene is a synthetic scene derived from terrain databases such as Digital Terrain Elevation Data (DTED), merged with three dimensional objects (scatterers), combined with an aerospace vehicle’s state (3 dimensional position, orientation, and 3 dimensional velocity), and presented as digital scenes suitable for conversion to RF signals for processing in real time by RF systems on the aerospace vehicle. Scene computation approaches should trade scene resolution and fidelity with computation time and effort for a given computing capability. Novel approaches can include precomputation of scenes given apriority knowledge on an aerospace vehicle’s state over a given timeframe. Precomputation approaches should focus on minimizing scene computation and storage while preserving the ability to present those scenes in real time to a system under test. Aerospace vehicle expected velocities may range from subsonic to hypersonic speeds. A suitable proposal will address at least one of the areas discussed above and show improvement over current state of the art. b. Validation & Verification for Trusted Autonomous Vehicles NSWC Crane seeks research concerning Validation & Verification (V&V) of autonomous vehicles that utilize Artificial Intelligence (AI) algorithms. A primary area of exploration involves the evaluation of V&V approaches as they apply to the development of AI “Trust” for operational situations. Of particular interest is the use of Live-Virtual-Constructive (LVC) methods and hardware-in-the-loop (HWIL) for pre-mission evaluation of learning integrity. Methods that utilize Unmanned Surface Vessel (USV) platforms for AI capability implementation will be of particular interest. Methods that utilize multiple laboratory facilities for federated learning are also of special interest.

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