Data Management And Bootstrapping Processing For Machine Learning And Classification Development

Abstract

A system for developing machine learning for use in the radiofrequency domain that produces a robust set of training data for machine learning from a small set of labelled training data that is bootstrapped with unlabeled electromagnetic environment data. A raw signal set is prepared from the labeled data and separately processed for any electromagnetic environment and interference signals as well as for a primary signal by applying the real electromagnetic environment data and then summed to generate a second data set that is larger than the first data set. Feature extraction is used to produce a bootstrapped labelled data set that is larger than the original labelled data set and that can be used as training data for machine language classification.

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