DocumentCode :
1600130
Title :
Improving RF-based device-free passive localization in cluttered indoor environments through probabilistic classification methods
Author :
Chenren Xu ; Firner, Bernhard ; Yanyong Zhang ; Howard, Richard ; Jun Li ; Xiaodong Lin
Author_Institution :
WINLAB, Rutgers Univ., North Brunswick, NJ, USA
fYear :
2012
Firstpage :
209
Lastpage :
220
Abstract :
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.
Keywords :
multipath channels; probability; radio equipment; radio links; Improving RF based device free passive localization; cell estimation; cluttered indoor environments; complex environment; discriminant analysis; fingerprint based methods; indoor localization; indoor space; multipath effect; probabilistic classification methods; radio device; radio devices; radio links; radio propagation; radio waves; Accuracy; Educational institutions; Indoor environments; Radio link; Radio transmitters; Receivers; Training; Device; Discriminant Analysis; Multi-path; RSS footprint; free Passive Localization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Processing in Sensor Networks (IPSN), 2012 ACM/IEEE 11th International Conference on
Conference_Location :
Beijing
Type :
conf
DOI :
10.1109/IPSN.2012.6920958
Filename :
6920958
Link To Document :
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