PROJECT TITLE :

High-Dimensional MVDR Beamforming: Optimized Solutions Based on Spiked Random Matrix Models - 2018

ABSTRACT:

Minimum variance distortionless response (MVDR) beamforming (or Capon beamforming) is among the foremost common adaptive array processing strategies because of its ability to supply noise resilience whereas nulling out interferers. A practical challenge with this beamformer is that it involves the inverse covariance matrix of the received signals, which should be estimated from data. Beneath modern high-dimensional applications, it's well-known that classical estimators will be severely suffering from sampling noise, that compromises beamformer performance. Here, we tend to propose a new approach to MVDR beamforming, that is suited to high-dimensional settings. In particular, by drawing an analogy with the MVDR problem and therefore the thus-called “spiked models” in random matrix theory, we propose robust beamforming solutions that are shown to outperform classical approaches (e.g., matched filters and sample matrix inversion techniques), in addition to additional robust solutions, such as methods based on diagonal loading. The key to our technique is the look of an optimized inverse covariance estimator, that applies eigenvalue clipping and shrinkage functions that are tailored to the MVDR application. Our proposed MVDR resolution is simple, in closed form, and straightforward to implement.