TY - JOUR
T1 - A genetic algorithm based direction finding technique with compensation of mutual coupling effects
AU - Lee, Kun Chou
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2003
Y1 - 2003
N2 - In this paper, genetic algorithms are applied to the direction finding of adaptive antenna arrays in the presence of mutual coupling. The received signals are compensated to eliminate the distortion due to mutual coupling effects. The compensation in this study is based on the concepts of moment methods without modifying the direction finding algorithm itself. After the received signals are compensated, the direction-of-arrival (DOA) of incident signals can be estimated by computing the power spectrum over the whole range of observation angles. Numerical examples show that this direction finding technique is accurate and with high resolution even in the presence of mutual coupling. With the use of genetic algorithms, the analyses in this paper do not require a suitable guess of an initial solution and there exist no gradient operations in the iteration procedures. Nearly global optimum solutions are obtained since genetic algorithms are inherent stochastic optimization processes.
AB - In this paper, genetic algorithms are applied to the direction finding of adaptive antenna arrays in the presence of mutual coupling. The received signals are compensated to eliminate the distortion due to mutual coupling effects. The compensation in this study is based on the concepts of moment methods without modifying the direction finding algorithm itself. After the received signals are compensated, the direction-of-arrival (DOA) of incident signals can be estimated by computing the power spectrum over the whole range of observation angles. Numerical examples show that this direction finding technique is accurate and with high resolution even in the presence of mutual coupling. With the use of genetic algorithms, the analyses in this paper do not require a suitable guess of an initial solution and there exist no gradient operations in the iteration procedures. Nearly global optimum solutions are obtained since genetic algorithms are inherent stochastic optimization processes.
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U2 - 10.1163/156939303772681479
DO - 10.1163/156939303772681479
M3 - Article
AN - SCOPUS:0346960929
SN - 0920-5071
VL - 17
SP - 1613
EP - 1624
JO - Journal of Electromagnetic Waves and Applications
JF - Journal of Electromagnetic Waves and Applications
IS - 11
ER -