Mathematical and Computational Foundations for Predictive Digital Twins
Abstract
Modern radars are increasingly being equipped with arbitrary waveform generators that enable generation of different waveforms across space, time, frequency, and polarization. The emergence of the multi-input multiple-output (MIMO) radar concept has brought the promise of achieving improved detectability, imaging resolution, and tracking performance. However, despite a great deal of progress in MIMO radar, many fundamental questions regarding the role and near-optimal use of the available degrees of freedom remain unanswered. The ability to exploit the new degrees of freedom available to MIMO radars in real-time is limited by the computational power available at the receiver and increased flexibility on transmission only exacerbates this problem unless waveforms are properly designed to simplify processing at the receiver. The complexity of the design problem motivates the assembly of full waveforms from a library of simple component waveforms with complementary properties. By complementary we mean that the waveform ambiguity functions interact in a desired way, and it is this complementarity that simplifies the design. By choosing to separate component waveforms across space, time, frequency, and polarization, or a combination of these, we can modularize the design problem. Modularity allows us to first analyze the effect of each control variable in isolation and then integrate across them to achieve desired response. This proposal develops a mathematical framework for modular waveform design and coordination and receiver signal processing for collocated MIMO radar, where the available degrees of freedom are efficiently utilized to enhance detection and imaging performance where necessary.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2025
- Source ID
- FA95502410327
Entities
People
- Karen Willcox
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Texas at Austin