DocumentCode :
1210384
Title :
Artificial neural network for AOA estimation in a multipath environment over the sea
Author :
Lo, Titus K Y ; Leung, Henry ; Litva, John
Author_Institution :
Commun. Res. Lab., McMaster Univ., Hamilton, Ont., Canada
Volume :
19
Issue :
4
fYear :
1994
fDate :
10/1/1994 12:00:00 AM
Firstpage :
555
Lastpage :
562
Abstract :
In this paper, we use a neural network to carry out angle-of-arrival (AOA) estimation in a multipath oceanic environment. In particular, the AOA problem is considered as a mapping from the space of AOA to the space of the sensor output. A neural network is used to determine the inverse mapping from the sensor output space to the space of AOA and this inversion is realized using a radial basis function (RBF) network. We will present the development of the RBF approach for AOA estimation. Simulations are carried out to understand the efficiency and performance of this method. Furthermore, real data are used to evaluate the RBF approach and the results demonstrate the robustness and effectiveness of this neural network method
Keywords :
digital simulation; direction-of-arrival estimation; feedforward neural nets; learning (artificial intelligence); multipath channels; signal processing; angle-of-arrival estimation; artificial neural network; effectiveness; inverse mapping; mapping; multipath environment; multipath oceanic environment; radial basis function network; robustness; Artificial neural networks; Biological neural networks; Intelligent networks; Maximum likelihood estimation; Neural networks; Robustness; Sensor arrays; Signal processing algorithms; Signal resolution; Spatial resolution;
fLanguage :
English
Journal_Title :
Oceanic Engineering, IEEE Journal of
Publisher :
ieee
ISSN :
0364-9059
Type :
jour
DOI :
10.1109/48.338391
Filename :
338391
Link To Document :
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