APPLICATION OF NEURAL NETWORKS IN THE ESTIMATION OF TWO-DIMENSIONAL TARGET ORIENTATION
Keywords:
APPLICATION OF NEURAL NETWORKS IN THE ESTIMATION OF TWO-DIMENSIONAL TARGET ORIENTATIONAbstract
A new method for the robust estimation of target orientation using measured radar cross section is proposed. The method is based on a Generalized Regression Neural Network (GRNN) scheme. The network is trained by the FFT modulus of bistatic radar cross section data sampled at the receiver positions. The target value to be trained is the angle between a defined target orientation and the incident wave. Results based on actual measurements are presented
Downloads
References
L. Marquez and T. Hill, “Function
approximation using back-propagation and
general regression neural networks,” Proceeding
of the Twenty-Sixth Hawaii International
Conference on System Sciences, Vol. 4, pp. 607-
, 1993.
S. P. Jacobs and J. A. O’Sullivan, “High-
resolution radar models for joint tracking and
recognition,” IEEE National Radar Conference,
pp. 99 –104, 1997.
S. P. Jacobs and J. A. O’Sullivan, “Automatic
target recognition using sequences of high-
resolution radar range-profiles ,” IEEE Trans.
Aerospace & Electronic Systems, Vol. 36, No. 2,
April 2000.
H. J. Li and S. H. Yang, “Using range profiles
as feature vectors to identify aerospace objects,”
IEEE Trans. Antennas & Propagat., Vol. 41,
No. 3, pp. 261-268, March 1993.
H. J. Lie and C. H. Ahn, “New iterative inverse
scattering algorithms based on neural networks,”
IEEE Trans. Magnetics, Vol. 30, No. 5,
September 1994.
I. Elshafiey, L. Udpa, and S. S. Upda,
“Application of neural networks to inverse
problems in electromagnetics,” IEEE Trans.
Magnetics, Vol. 30, No. 5, September 1994.
S.Caorsi and P.Gamba, “Detection of dielectric
cylinder by a neural network approach,” IEEE
Trans. Geoscienece & Remote Sensing, Vol. 37,
pp. 820-827, 1999.
S.Chakrabarti, N. Bindal, and K.Theagharajan,
“Robust radar target classifier using artificial
neural networks,” IEEE Trans. Neural Networks,
Vol. 6, No. 3, May 1995.
D. F. Specht, “A general regression neural
network,” IEEE Trans. Neural Networks, Vol. 2,
No. 6, pp. 568 –576, November 1991.
A. H. El Zooghby, C. G. Christodoulou, and M.
Georgiopoulos, “Performance of radial-basis
function networks for direction of arrival
estimation with antenna arrays,” IEEE Trans.
Antennas & Propagat., Vol. 45, No. 11,
November 1997.
E. Parzen, “On estimation of a probability
density function and mode,” Ann. Math. Statist.,
Vol. 33, pp. 1065-1076, 1962
