Use of probabilistic neural networks for emitter correlation

P. Susie Maloney

doi:10.1117/12.21188

1 August 1990 Use of probabilistic neural networks for emitter correlation

P. Susie Maloney

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21188
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

Abstract

The Probabilistic Neural Network (PNN) as described by Specht''3 has been successfully applied to a number of emitter correlation problems involving operational data for training and testing of the neural net work. The PNN has been found to be a reliable classification tool for determining emitter type or even identifying specific emitter platforms given appropriate representative data sets for training con sisting only of parametric data from electronic intelligence (ELINT) reports. Four separate feasibility studies have been conducted to prove the usefulness of PNN in this application area: . Hull-to-emitter correlation (HULTEC) for identification of seagoing emitter platforms . Identification of landbased emitters from airborne sensors . Pulse sorting according to emitter of origin . Emitter typing based on a dynamically learning neural network. 1 .

Citation Download Citation

P. Susie Maloney "Use of probabilistic neural networks for emitter correlation", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); https://doi.org/10.1117/12.21188

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