DocumentCode :
2146340
Title :
Object classification by system identification and feature extraction methods applied to estimation of SEM parameters
Author :
Brooks, John W. ; Maier, Mark W.
Author_Institution :
Dynetics Inc., Huntsville, AL, USA
fYear :
1994
fDate :
29-31 Mar 1994
Firstpage :
200
Lastpage :
205
Abstract :
Literature on estimating Singularity Expansion Method (SEM) parameters to date has been divided into two distinct areas: one which estimates exponentially damped sinusoids of synthetic pulses when the parameters are known a priori, and the other, estimation of “SEM parameters” from laboratory measurements of complex bodies with no a priori knowledge of the “true” SEM parameters. In this paper, we apply an optimal Instrumental Variables (IV) method to the estimation of SEM parameters for two classes of conducting bodies of revolution (BOR), cones and cylinders, for which the theoretical SEM parameters have been computed. The estimated SEM parameters are compared to the theoretical values for each class, and then classification schemes are proposed by which those parameters are used to distinguish the two target classes
Keywords :
feature extraction; numerical analysis; parameter estimation; pattern recognition; radar theory; signal processing; SEM parameter estimation; classification schemes; complex bodies; conducting bodies of revolution; cones; cylinders; exponentially damped sinusoids; feature extraction methods; laboratory measurements; object classification; optimal instrumental variables method; radar target identification; singularity expansion method; synthetic pulses; system identification; target classes; Area measurement; Feature extraction; Laboratories; Libraries; Magnetic resonance; Parameter estimation; Pulse measurements; Radar scattering; Radar theory; System identification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Radar Conference, 1994., Record of the 1994 IEEE National
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-1438-7
Type :
conf
DOI :
10.1109/NRC.1994.328124
Filename :
328124
Link To Document :
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