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DEVICE-FREE LOCALIZATION ROBUST TO ENVIRONMENTAL CHANGES
DEVICE-FREE LOCALIZATION ROBUST TO ENVIRONMENTAL CHANGES
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机译:无设备本地化对环境变化具有强大
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摘要
A method of location determination with a WiFi transceiver and an AI model includes jointly training, based on various losses: a feature extractor, a location classifier, and a domain classifier. The domain classifier may include a first domain classifier and a second domain classifier. The losses used for training tend to cause feature data from the feature extractor to cluster even if a physical object in an environment has moved after training is completed. Then, the location classifier is able to accurately estimate the position of, for example, a person in a room, even if a door or window has changed from open to close or close to open between the time of training and the time of estimating the person's position.
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