Data-Driven Improved Space-Time Adaptive Processing (STAP) Radar

Abstract

We will design new physics infused data-driven approaches to improve classical radar algorithms. In particular, we will mainly focus on Space-Time Adaptive Processing (STAP) radar which is widely employed in many applications. To this end, we will use the knowledge of location and velocity of airborne vehicle, and the knowledge of environment incorporated in the RFVIEW platform (Developed by ISLinc) in order to estimate a representative set of dominant scatterers. This can be done totally off-line and without any transmission or reception of radar signals. Having this knowledge at hand, we will schedule the transmission algorithm development.

Document Details

Document Type
DoD Grant Award
Publication Date
Jan 21, 2022
Source ID
FA95502110235XX0

Entities

People

  • Vahid Tarokh

Organizations

  • Air Force Office of Scientific Research
  • Duke University
  • United States Air Force

Tags

Readers

  • Neural Network Machine Learning.
  • Radar Systems Engineering.

Technology Areas

  • Space
  • Space - Space Objects