Exploiting human mobility trajectory information in indoor device-free passive tracking

Authors:
Chenren Xu;Bernhard Firner;Yanyong Zhang;Richard Howard;Jun Li
Affiliations:
Rutgers University, North Brunswick, NJ, USA;Rutgers University, North Brunswick, NJ, USA;Rutgers University, North Brunswick, NJ, USA;Rutgers University, North Brunswick, NJ, USA;Electrical and Computer Engineering, North Brunswick, NJ, USA
Venue:
Proceedings of the 11th international conference on Information Processing in Sensor Networks
Year:
2012

Citing 2
Cited 0

Challenges: device-free passive localization for wireless environments

Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods

Proceedings of the 11th international conference on Information Processing in Sensor Networks

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Abstract

Device-free passive (DfP) localization is proposed to localize human subjects indoors by observing how the subject disturbs the pattern of the radio signals without having the subject wear a tag. In our previous work, we have proposed a probabilistic classification based DfP technique, which we call PC-DfP in short, and demonstrated that PC-DfP can classify which cell (32 cells in total) is occupied by the stationary subject with an accuracy as high as 97.2% in a one-bedroom apartment. In this poster, we focus on extending PC-DfP to track a mobile subject in indoor environments by taking into consideration that a human subject's locations should form a continuous trajectory. Through experiments in a 10 × 15 meters open plan office, we show that we can achieve better accuracies by exploiting the property of continuous mobility trajectories.