title: Robust radar target classifier using artificial neural networks.
abstract: In this paper an artificial neural network (ANN) based radar target classifier is presented, and its performance is compared with that of a conventional minimum distance classifier. Radar returns from realistic aircraft are synthesized using a thin wire time domain electromagnetic code. The time varying backscattered electric field from each target is processed using both a conventional scheme and an ANN-based scheme for classification purposes. It is found that a multilayer feedforward ANN, trained using a backpropagation learning algorithm, provides a higher percentage of successful classification than the conventional scheme. The performance of the ANN is found to be particularly attractive in an environment of low signal-to-noise ratio. The performance of both methods are also compared when a preemphasis filter is used to enhance the contributions from the high frequency poles in the target response.
authors.affiliation: Dept. of Electr. and Comput. Eng., Kansas Univ., Lawrence, KS.
authors.forename: S
authors.initials: S
authors.lastname: Chakrabarti
authors.forename: N
authors.initials: N
authors.lastname: Bindal
authors.forename: K
authors.initials: K
authors.lastname: Theagharajan