By Topic

Direction of arrival and state of polarization estimation using Radial Basis Function Neural Network (RBFNN)

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

4 Author(s)
Zainud-Deen, S.H. ; Fac. of Electron. Eng., Menoufia Univ., Menouf ; Malhat, H.A. ; Awadalla, K.H. ; El-Hadad, E.S.

A neural network architecture is applied to the problem of direction of arrival (DOA) and state of polarization estimation using a uniform circular cross and tri-crossed-dipoles antenna array. A three layer radial basis function network (RBFN) is trained with input output pairs. The network is then capable of estimating DOA not included in the training set through generalization and the corresponding state of polarization. This approach reduces the extensive computations required by conventional super resolution algorithms such as MUSIC and is easier to implement in real-time applications. The results suggest that the performance of the RBFNN method approaches the exact values. In real time, fast convergence rates of neural networks will allow the array to track mobile sources.

Published in:

Radio Science Conference, 2008. NRSC 2008. National

Date of Conference:

18-20 March 2008