By Topic

A Robust Adaptive Dimension Reduction Technique With Application to Array Processing

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
$31 $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)
Hassanien, A. ; Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, AB ; Vorobyov, S.A.

We develop a data-adaptive dimension reduction algorithm that is robust against out-of-sector sources in application to array processing. The dimension reduction is done as a linear transformation (matrix filter). The matrix filter is designed adaptively such that the signal power within a certain sector is preserved while the out-of-sector power is maximally rejected. The columns of the beamspace matrix are designed sequentially, one column at a time. This sequential implementation is carried out by imposing orthogonality constraints between beamspace matrix columns. Hence, the white noise property at the output of the beamspace preprocessor is preserved. The latter is important for subsequent data processing. The proposed algorithm is computationally less expensive as compared to the existing data-adaptive beamspace design techniques. Simulation results validate the robustness of the developed algorithm, and they show its effectiveness and superiority to the existing algorithms.

Published in:

Signal Processing Letters, IEEE  (Volume:16 ,  Issue: 1 )