Device-free localization robust to environmental changes

An electronic device comprising:

• one or more memories, wherein the one or more memories comprise instructions;
• a WiFi transceiver;
• and
• one or more processors, wherein the one or more processors are configured to execute the instructions to:
• jointly train, based on a cross-domain loss, a clustering loss, and a triplet loss:
• a feature extractor comprising at least a feature extraction layer of an AI model,
• a location classifier comprising at least a location classifier layer of the AI model, and
• a domain classifier comprising at least a domain classifier layer of the AI model;
• classify, using the location classifier layer of the AI model and based on a first set of feature data from the feature extraction layer, first channel state information (CSI) data to obtain a first estimated location label of a first person in a first environment;
• receive, via the WiFi transceiver, second CSI data associated with a second person at a first physical location and a second environment;
• obtain, using the feature extraction layer of the AI model and based on operating on the second CSI data, a second set of feature data;
• classify, using the location classifier layer of the AI model and based on the second set of feature data from the feature extraction layer, the second CSI data to obtain a second estimated location label of the second person, and
• output, by a visible indication or an audible indication, a second indicator of the second estimated location label of the second person.

A non-transitory computer-readable storage medium storing instructions configured to cause a processor to:

• jointly train, based on a cross-domain loss, a clustering loss, and a triplet loss:
• a feature extractor comprising at least a feature extraction layer of an AI model,
• a location classifier comprising at least a location classifier layer of the AI model, and
• a domain classifier comprising at least a domain classifier layer of the AI model;
• classify, using the location classifier layer of the AI model and based on a first set of feature data from the feature extraction layer, first channel state information (CSI) data to obtain a first estimated location label of a first person in a first environment;
• receive, via a WiFi transceiver, second CSI data associated with a second person at a first physical location and a second environment;
• obtain, using the feature extraction layer of the AI model and based on operating on the second CSI data, a second set of feature data;
• classify, using the location classifier layer of the AI model and based on the second set of feature data from the feature extraction layer, the second CSI data to obtain a second estimated location label of the second person, and
• output, by a visible indication or an audible indication, a second indicator of the second estimated location label of the second person.