论文信息 - Classification of Cylindrical Targets above Perfectly Conducting Flat Surfaces by Statistical Neural Networks

Classification of Cylindrical Targets above Perfectly Conducting Flat Surfaces by Statistical Neural Networks

This paper evaluates the radar target classification performance of neural networks. A set of features are derived from scattered fields calculated by using the image technique formulation and Moment Method (MoM). Statistical neural networks that utilize the feature set are proposed for target classification. The database contains a finite number of samples of three cylindrical targets at certain angles. A portion of the database is used to train the network and the rest is used to test the performance of the neural network for target classification. This work aims to find the right target above a perfectly conducting (PEC) flat surface from the scattered .field values.

Ahmet Kizilay | Senem Makal

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