Proceedings of the 11th international conference on Information Processing in Sensor Networks
Passive detection of situations from ambient FM-radio signals
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Device-free and device-bound activity recognition using radio signal strength
Proceedings of the 4th Augmented Human International Conference
SCPL: indoor device-free multi-subject counting and localization using radio signal strength
Proceedings of the 12th international conference on Information processing in sensor networks
Joint localization and activity recognition from ambient FM broadcast signals
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
WASA'13 Proceedings of the 8th international conference on Wireless Algorithms, Systems, and Applications
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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RF-based transceiver-free object tracking, originally proposed by the authors, allows real-time tracking of a moving object, where the object does not have to be equipped with an RF transceiver. Our previous algorithm, the best cover algorithm, suffers from a drawback, i.e., it does not work well when there are multiple objects in the tracking area. In this paper, we propose a localization model of distance, transmission power and the signal dynamics caused by the objects. The signal dynamics are derived from the measured Radio Signal Strength Indication (RSSI). Using this new model, we propose the “probabilistic cover algorithm” which is based on distributed dynamic clustering thus it can dramatically improve the localization accuracy when multiple objects are present. Moreover, the probabilistic cover algorithm can reduce the tracking latency in the system. We argue that the small overhead of the proposed algorithm makes it scalable for large deployment. Experimental results show that in addition to its ability to identify multiple objects, the tracking accuracy is improved at a rate of 10% to 20%.