Fully-Adaptive Radar Modeling and Simulation Development

ABSTRACT: The goal of this project is to develop a modeling and simulation (M&S) capability for distributed, fully adaptive radar (FAR) that will enable algorithm development and testing on simulated, previously collected, and real-time streaming data.The foundation of the distributed sensor simulation is a previously developed M&S tool for generating ground moving target indicator (GMTI) multiple input multiple output (MIMO) radar clutter samples using a sophisticated a physics-based bistatic scattering model (PBSM) capable of predicting the dynamically varying clutter patch statistical and spectral properties.A real-time experimental sensing capability is provided by the Cognitive Radar Engineering Workspace (CREW) in the Cognitive Radar Lab at The Ohio State University, one of the first cognitive-enabled research test beds in the world. The M&S architecture will allow for transparent switching between the data sources, as well as between FAR algorithms that drive the sensing.In Phase I, we will develop the architecture, the application programming interface (API) layers for simulated and experimental data sources, and a basic distributed FAR algorithm to control the sensing, and demonstrate the real-time operation in the Cognitive Radar Lab.; BENEFIT: Radar systems are crucial for robust surveillance, target acquisition, and reconnaissance in all weather conditions and over wide ranges of interest.Anti-access/area denial (A2/AD) environments are especially challenging, therefore it is necessary to develop innovative signal and data processing techniques to provide the next level of sensing capabilities to the warfighter.Distributed radar and fully adaptive radar (FAR) systems seek to exploit all available degrees of freedom on transmit and receive in order to maximize radar system performance and thus offer much promise for improved sensing as well as the creation of new sensing modalities.Modeling and simulation (M&S) and real-time experimentation play a critical role for FAR algorithm development due to the need for dynamic generation of representative scenarios. A key gap in the Air Forces previously developed radar M&S tools is the lack of a comprehensive, dynamic distributed radar scenario generation capability. This project will develop a fundamental M&S capability for distributed, fully adaptive radar that will enable algorithm development and testing on simulated, previously collected, and real-time streaming data.The M&S architecture will allow for transparent switching between the data sources, as well as between FAR algorithms that drive the sensing.The ability to easily interchange sensing and processing modules will enable collaboration between researchers in industry, academia, and the Air Force for development of improved radar sensing capabilities.