Device Free

Device-free localization (DFL) aims to pinpoint individuals within an environment without requiring them to carry any electronic devices, leveraging existing wireless infrastructure. Current research focuses on improving accuracy and robustness by employing advanced signal processing techniques, such as those based on time-of-flight measurements and error vector spectrum analysis, often integrated with deep learning models like stacked denoising autoencoders and graph neural networks. This field is significant for its potential applications in various sectors, including security, healthcare, and the Internet of Things, offering a privacy-preserving alternative to device-based tracking systems. Furthermore, efficient data collection methods, such as few-shot transfer learning, are actively being developed to reduce the cost and effort associated with deploying DFL systems.