A new parametric GLRT for multichannel adaptive signal detection

  • Authors:

  • Pu Wang;Hongbin Li;Braham Himed

  • Affiliations:

  • Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ;Radar Signal Processing Technology Branch, Air Force Research Laboratory, Dayton, OH

  • Venue:
  • IEEE Transactions on Signal Processing
  • Year:
  • 2010

Quantified Score

Hi-index 35.68

Visualization

Abstract

A parametric generalized likelihood ratio test (GLRT) for multichannel signal detection in spatially and temporally colored disturbance was recently introduced by modeling the disturbance as a multichannel autoregressive (AR) process. The detector, however, involves a highly nonlinear maximum likelihood estimation procedure, which was solved via a two-dimensional iterative search method initialized by a suboptimal estimator. In this paper, we present a simplified GLRT along with a new estimator for the problem. Both the estimator and the GLRT are derived in closed form at considerably lower complexity. With adequate training data, the new GLRT achieves a similar detection performance as the original one. However, for the more interesting case of limited training, the original GLRT may become inferior due to poor initialization. Because of its simpler form, the new GLRT also offers additional insight into the parametric multichannel signal detection problem. The performance of the proposed detector is assessed using both a simulated dataset, which was generated using multichannel AR models, and the KASSPER dataset, a widely used dataset with challenging heterogeneous effects found in real-world environments.