Print Email Facebook Twitter Physics-informed Data Augmentation for Human Radar Signatures Title Physics-informed Data Augmentation for Human Radar Signatures Author Focante, Edoardo (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Leus, G.J.T. (mentor) De Martín, Lilian (mentor) Fioranelli, F. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Signals and Systems Date 2023-01-24 Abstract In recent years, neural networks (NNs) have seen a surge in popularity due to their ability to model complex patterns and relationships in data. One of the challenges of using NNs is the requirement for large amounts of labelled data to train the model effectively. In many real-world applications such as radar, labelled data may be scarce due to the high cost of acquiring measurements together with privacy and security concerns.To overcome the lack of data, researchers have resorted to data augmentation (DA), a technique that aims to solve the problem at the root by generating new training samples by leveraging the available ones. In computer vision, image transformations and generative networks are used to perform DA. These techniques, however, may lead to the production of physically unfeasible samples that may hinder the generalization capabilities of classifiers in domains where the data has an underlying physical meaning.Physics-informed machine learning aims to incorporate physical prior knowledge and governing equations of the target domain into the machine learning pipeline to improve the performance of NNs in fields with limited available data but with well-defined physical models. In this thesis, physics-informed DA in the radar domain is addressed to improve the task of classifying armed and unarmed walking individuals through micro-Doppler spectrograms. To begin with, the usage of model-driven micro-Doppler radar simulations to improve the existing generative augmentations is investigated. After introducing several generative NN architectures, the quality and diversity of the produced synthetic spectrograms are evaluated together with their effect on the downstream classification task.Next, the impact of visual transformations on micro-Doppler spectrograms is studied. Their effect on the underlying physics of the micro-Doppler spectrograms and their impact on feature extraction is assessed. Based on the results, a new DA technique is devised to improve the feature extraction process by informed segmentation of the input spectrogram, halving the size of the NN model and reducing the risk of overfitting. Subject Data Augmentationmicro-DopplerGenerative Adversarial Networkphysics-informed neural networkshuman activity recognitionDeep Learning To reference this document use: http://resolver.tudelft.nl/uuid:4dc96482-8a64-4479-ba90-b5c85427b33d Embargo date 2024-01-24 Part of collection Student theses Document type master thesis Rights © 2023 Edoardo Focante Files PDF Thesis_Focante.pdf 9.44 MB Close viewer /islandora/object/uuid%3A4dc96482-8a64-4479-ba90-b5c85427b33d/datastream/OBJ/view