Presence detection by means of RF waveform classification

Abstract: This master thesis investigates the possibility to automatically label and classify radio waves for presence detection, where the objective is to obtain information about the number of people in a room based on channel estimates. Labeling data for machine learning is time consuming and tedious process. To address this two approaches are evaluated. One was to develop a framework to generate labels with the aid of computer vision AI. The other relies on unsupervised learning classifiers complemented with heuristics to generate the labels. The investigation also studies the performance of the classifiers as a function of the TX/RX configuration, SNR, number of consecutive samples in a feature vector, bandwidth and frequency band. When someone moves in a room the propagation environment changes and induces variations in the channel estimates, compared to when the room is empty. These variations are the fundamental concept that is exploited in this thesis. Two methods are suggested to perform classification without the need of training data. The first uses random trees embeddings to construct a random forest without labels and the second using statistical bootstrapping with a random forest classifier. The labels used for annotation indicate whether were zero, one or two people in the room. The performance of binary and non-binary classification is evaluated both for the two blind detection models, as well as the performance of the unsupervised learning techniques Kmeans and self-organizing maps. For classification both supervised and unsupervised learning use random forest classifiers. Results show that random forest classifiers perform well for this kind of problem, and that random tree embeddings are able to extract relational data that could be used for automatic labeling of the data.