Device-free and device-bound activity recognition using radio signal strength
Proceedings of the 4th Augmented Human International Conference
Enhancing the performance of indoor localization using multiple steady tags
Pervasive and Mobile Computing
Joint localization and activity recognition from ambient FM broadcast signals
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
RF-Based device-free recognition of simultaneously conducted activities
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
CoSDEO 2013: device-free radio-based recognition
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
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A wireless network can use the variance of measured received signal strength (RSS) on the links in a network to infer the locations of people or objects moving in the network deployment area. This paper provides a statistical model for the RSS variance as a function of a person's position with respect to the transmitter (TX) and receiver (RX) locations. We show that the ensemble mean of the RSS variance has an approximately linear relationship with the expected value of total affected power (ETAP), for a range of ETAP. We derive approximate expressions for the ETAP as a function of the person's position, for scattering and reflection, which are tested via simulation. Counterintuitively, we show that reflection, not scattering, causes the RSS variance contours to be shaped similar to Cassini ovals. Results reported in past literature and from a new experiment reported in this paper are shown to be as predicted by the analysis.