By Topic

Beamforming in additive α-stable noise using fractional lower order statistics (FLOS)

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
B. Kannan ; Dept. of Eng., Cambridge Univ., UK ; W. J. Fitzgerald

Non-Gaussian statistical signal processing is important when signal or noise deviates from the ideal Gaussian model. Stable distributions are among the most important non-Gaussian models. Minimum noise power, minimum variance distortionless signal response (MNPDR, MVDR) and minimum mean square error (MMSE) beamformers are widely used to estimate the signals in Gaussian noise environments. In this paper, we present a beamforming technique for additive symmetric α-stable (SαS) noise environments. This new technique uses FLOS to formulate a nonlinear cost function which is then minimised to get an optimum weight vector for the array of sensors while the gain in the desired look direction is constrained to be unity. As this nonlinear constrained optimisation problem doesn't have a closed form solution, we use a gradient-based algorithm to estimate the weight vectors. This new algorithm is computationally efficient and can be used with a wide range of stable noise models

Published in:

Electronics, Circuits and Systems, 1999. Proceedings of ICECS '99. The 6th IEEE International Conference on  (Volume:3 )

Date of Conference: