Robust M-Estimation for Array Processing: A Random Matrix Approach

Details Robust M-Estimation for Array Processing: A Random Matrix Approach Robust M-Estimation for Array Processing: A Random Matrix Approach

2012-04-24

arxiv.org/abs/1204.5320v1

Computer Science

Information Theory

submitted to IEEE Transaction on Signal Processing

Scientific paper

This article studies the limiting behavior of a robust M-estimator of population covariance matrices as both the number of available samples and the population size are large. Using tools from random matrix theory, we prove that the difference between the sample covariance matrix and the robust M-estimator tends to zero in spectral norm, almost surely. This result is applied to prove that recent subspace methods arising from random matrix theory can be made robust without altering their first order behavior.

Affiliated with

Also associated with

No associations

Rating

Robust M-Estimation for Array Processing: A Random Matrix Approach does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Robust M-Estimation for Array Processing: A Random Matrix Approach, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust M-Estimation for Array Processing: A Random Matrix Approach will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-519491