NDLI: Automatic target recognition of aircrafts using translation invariant features and neural networks

Content Provider	IEEE Xplore Digital Library
Author	Zun-hua Guo Shao-hong Li Wei-xin Xie
Copyright Year	2008
Description	Author affiliation: Sch. of Electron. & Inf. Eng., Beihang Univ., Beijing (Shao-hong Li) \|\| ATR Nat. Defence key Lab., Shenzhen Univ., Shenzhen (Zun-hua Guo; Wei-xin Xie)
Abstract	Automatic target recognition (ATR) of aircrafts using translation invariant features derived from high range resolution (HRR) profiles and multilayered neural network is presented in this paper. The HRR profile sequences are translation variant in the range resolution cell because of the non-cooperative target maneuvering. The differential power spectrum (DPS) is introduced to extract the translation invariant features. Several learning algorithms of feed-forward neural network are implemented to determine an optimal choice in the recognition phase. The range profiles are obtained using the two-dimensional backscatters distribution data of four different scaled aircraft models. Simulations are presented to evaluate the classification performance with the DPS based features and neural networks. The results show that this method is effective for the application of radar target recognition.
Starting Page	2271
Ending Page	2274
File Size	166657
Page Count	4
File Format	PDF
ISBN	9781424421787
DOI	10.1109/ICOSP.2008.4697602
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target recognition Neural networks Feature extraction Radar scattering Aircraft propulsion Airborne radar Aerospace electronics Radar imaging Multi-layer neural network Feedforward systems high range resolution profiles automatic target recognition feature extraction neural networks
Content Type	Text
Resource Type	Article

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