Sina Einavi Pour
Enhancing Indoor Human Localization with Echo State Networks and Temporal Convolutional Networks.
Rel. Mihai Teodor Lazarescu. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Elettronica (Electronic Engineering), 2024
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Abstract
Indoor human localization has gained significant importance due to its wide range of applications, including smart environments, health monitoring, and security systems. Accurate indoor positioning remains a challenging task due to signal interference, complex environments, and the need for real-time data processing. Localization systems can be classified into active and passive methods, each with unique characteristics and applications. Active systems require external signal emissions, using transmitters and receivers to determine an object or person’s location. While effective, these systems involve higher infrastructure costs due to additional equipment, and they require regular maintenance and user interaction, which can impact convenience. In contrast, passive localization systems rely on natural signals or environmental interactions, reducing installation and maintenance costs, and offering a less intrusive experience as no wearable tags are needed.
Passive methods such as infrared, ultrasound, and radar sensing, though efficient, often face limitations like high costs or privacy concerns
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