Accession Number:

ADA621974

Title:

Random Matrix Theoretic Approaches to Sensor Fusion for Sensing and Surveillance in Highly Cluttered Environments

Descriptive Note:

Final performance rept. 15 May 2012-14 May 2015

Corporate Author:

MICHIGAN UNIV ANN ARBOR

Personal Author(s):

Report Date:

2015-08-24

Pagination or Media Count:

19.0

Abstract:

Powerful algorithms that are able to detect, estimate, and classify increasingly weaker signals buried in noise are a critical technological component of several important US Air Force technologies such as SAR, MIMO radar and hyperspectral imaging among others. Advances in VLSI are making sensors cheaper and easier to deploy in increasing numbers. What limits our ability to detect and discriminate weaker statistical signatures from statistical clutter is not the sensor count but algorithm-independent statistical limits associated with the finite number of snapshots over which the effects of clutter can be averaged out. In the work supported by the Young Investigator Award, we have characterized the fundamental limits of statistical estimation and detection for a variety of problems of direct relevance to the US Air Force. These are organized into two thrusts Thrust 1 Characterization of fundamental limits and improved algorithms for detection, estimation and classification of matrix-valued signals buried in noise with missing data and, Thrust 2 Characterization of fundamental limits of and improved algorithms for transmission of energy through highly scattering random media.

Subject Categories:

  • Statistics and Probability
  • Miscellaneous Detection and Detectors
  • Active and Passive Radar Detection and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE