The active badge location system
ACM Transactions on Information Systems (TOIS)
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
A Statistical Modeling Approach to Location Estimation
IEEE Transactions on Mobile Computing
LANDMARC: Indoor Location Sensing Using Active RFID
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Using Area-Based Presentations and Metrics for Localization Systems in Wireless LANs
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
The Horus WLAN location determination system
Proceedings of the 3rd international conference on Mobile systems, applications, and services
An RF-Based System for Tracking Transceiver-Free Objects
PERCOM '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications
Challenges: device-free passive localization for wireless environments
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Dynamic clustering for tracking multiple transceiver-free objects
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
Radio Tomographic Imaging with Wireless Networks
IEEE Transactions on Mobile Computing
A deterministic large-scale device-free passive localization system for wireless environments
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Indoor localization without the pain
Proceedings of the sixteenth annual international conference on Mobile computing and networking
RASS: A real-time, accurate and scalable system for tracking transceiver-free objects
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
Location sensing and privacy in a context-aware computing environment
IEEE Wireless Communications
Exploiting human mobility trajectory information in indoor device-free passive tracking
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Towards robust device-free passive localization through automatic camera-assisted recalibration
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
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
Radio tomographic imaging and tracking of stationary and moving people via kernel distance
Proceedings of the 12th international conference on Information processing in sensor networks
It's tea time: do you know where your mug is?
Proceedings of the 5th ACM workshop on HotPlanet
Device-free people counting and localization
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
From RSSI to CSI: Indoor localization via channel response
ACM Computing Surveys (CSUR)
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Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood. We validate our method in a one-bedroom apartment that has 8 transmitters, 8 receivers, and a total of 32 cells that can be occupied. We show that our method can correctly estimate the occupied cell with a likelihood of 97.2%. Further, we show that the accuracy remains high, even when we significantly reduce the training overhead, consider fewer radio devices, or conduct a test one month later after the training. We also show that our method can be used to track a person in motion and to localize multiple people with high accuracies. Finally, we deploy our method in a completely different commercial environment with two times the area achieving a cell estimation accuracy of 93.8% as an evidence of applicability to multiple environments.