NDLI: The application of fusion of multiple aspect scattered data to PMUSIC-vector based electromagnetic target identification

Content Provider	IEEE Xplore Digital Library
Author	Secmen, M.
Copyright Year	2011
Description	Author affiliation: Electrical and Electronics Engineering Department, Yasar University, Izmir, Turkey (Secmen, M.)
Abstract	This paper presents an electromagnetic target identification method in the resonance scattering region, which uses pseudospectrum multiple signal classification (PMUSIC) vectors as feature vectors. These feature vectors are obtained with the fusion of multiple aspect scattered fields of the targets into one covariance matrix for each target. The proposed method initially stores the suitable late-time interval of the scattered fields of a target obtained at different aspect angles. Then, the correlation information of these signals is gathered into one covariance matrix by a simple fusion technique. By using this covariance matrix and PMUSIC algorithm, feature vectors of the targets are extracted and stored to database. In the test phase, PMUSIC algorithm is applied to the same suitable late-time interval of test field (signal) belonging to a test target and test (PMUSIC) vector is evaluated. Finally, the decision about the test target is given according to the highest correlation coefficient between feature vectors and test vector. The proposed method is demonstrated for small-scale airplane targets modeled by thin wires. It is shown that in addition to its short runtime, the method gives satisfactory accuracy rates even for low signal-to-noise (SNR) ratios.
Starting Page	2750
Ending Page	2753
File Size	238612
Page Count	4
File Format	PDF
ISBN	9781424495627
ISSN	19471491
e-ISBN	9781424495634
DOI	10.1109/APS.2011.5997095
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Covariance matrix Feature extraction Airplanes Resonant frequency Runtime Correlation Multiple signal classification
Content Type	Text
Resource Type	Article

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