Compressive Sensing Waveform Studies for Enhanced RADAR Performance (WARP)

Compressive sensing introduces a new paradigm in RADAR image formation.  This approach to image formation supports new concepts for collection of RADAR imagery. The theoretical framework being developed at MTRI allows us to define optimal collection strategies for imaging from networks of RADARs.

Overview

• Many scenes / signals are compressible – can be expressed as a linear combination of a small number of components (e.g., JPEG).
• Compressive sensing (CS) moves the compression to the sensor before an image is formed.

Active Areas of Research at MTRI

 General theoretical developments in compressive sensing with particular focus on distributed RADAR sensing/imaging. Incorporates

1.  Scene phenomenology (monostatic/bistatic)
2.  Waveforms (e.g., chirp, random, Alltop)
3.  Joint geometry of radars/scene

Application of compressive sensing framework to the distributed RADAR network problem.  Framework is providing useful insights into the role of waveform design and the resulting performance of the distributed imaging system.

The figures above result from image formation processing of synthetic RADAR returns from antennas illuminating six point targets.   Five antennas form a sparse array with randomly chosen azimuths spanning 18º. 

For Additional Information

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Nikola Subotic, Ph.D.
Co-Director
734.913-6859
nikola.subotic@mtu.edu

Joseph Burns, Ph.D.
Senior Research Scientist
734.913.6857
joseph.burns@mtu.edu

Many environments, including cities, have sparse representations in collected RADAR signals – permitting good representation with wise selection of parameters.  Below is a collection of radar signals of a building.