Machine learning supported detection of incoupling interfering signals through autoencoders

The detection of anomalies in automotive sensor signals distorted by intentional electromagnetic interference (IEMI) is investigated through the support of autoencoders. These are designed to extract the most important features of data in a dimensionally reduced latent representation. Furthermore, several classification methods for electromagnetic signal disturbances are analyzed and compared in this compressed latent space. The performance of the methods is compared to a baseline model and evaluated using different metrics. The aim of the investigated methods is to achieve a high recall (sensitivity) and minimal false negative misclassifications, thus detecting all possible anomalies, for which a neural network classifier is shown to be the best-performing model.

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