FLoc: Device-free passive indoor localization in complex environments

Localization in complex indoor environments with RF signal is a challenging task. Via this technique, the signal is easily affected by obstacles and environmental noise due to the broadcast nature of RF signal transmission. In this paper, we observe that seismic signal is more oriented than the RF signal, and thus is more suitable for localization in an complex indoor environment with obstacles. Motivated by this observation, we propose a device-free passive indoor localization system, namely Floc, that can fight against environmental impact and achieve high localization accuracy. FLoc is composed of three modules, which are sensing module that collects seismic signal from footsteps, footstep detection module that remove the environmental impact and recover the clean footsteps, and localization module that leverages seismic signal from footsteps for positioning. We implement FLoc on credit-card sized single-board computer Raspberry Pi equipped with geophones. To verify the effectiveness of our system, we conduct extensive experiments for different scenario in a complex indoor environment with the area of 6 × 8 square meter. The experimental results demonstrates that Floc can achieve up to 7cm localiztion accuracy on average, and can outperform the existing acoustic signal-based localization techniques.

[1]  Hae Young Noh,et al.  Characterizing wave propagation to improve indoor step-level person localization using floor vibration , 2016, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[2]  R. Michael Buehrer,et al.  Indoor positioning from vibration localization in smart buildings , 2016, 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS).

[3]  Kaishun Wu,et al.  CSI-Based Indoor Localization , 2013, IEEE Transactions on Parallel and Distributed Systems.

[4]  Min Gao,et al.  FILA: Fine-grained indoor localization , 2012, 2012 Proceedings IEEE INFOCOM.

[5]  Kaishun Wu,et al.  FIFS: Fine-Grained Indoor Fingerprinting System , 2012, 2012 21st International Conference on Computer Communications and Networks (ICCCN).

[6]  Gervasio Prado,et al.  Footstep detection and tracking , 2001, SPIE Defense + Commercial Sensing.

[7]  Xiang Li,et al.  Dynamic-MUSIC: accurate device-free indoor localization , 2016, UbiComp.

[8]  Lu Wang,et al.  NomLoc: Calibration-Free Indoor Localization with Nomadic Access Points , 2014, 2014 IEEE 34th International Conference on Distributed Computing Systems.

[9]  Yunhao Liu,et al.  Swadloon: Direction Finding and Indoor Localization Using Acoustic Signal by Shaking Smartphones , 2015, IEEE Transactions on Mobile Computing.

[10]  Lu Wang,et al.  Pilot: Passive Device-Free Indoor Localization Using Channel State Information , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.

[11]  Alex Pakhomov,et al.  New seismic sensors for footstep detection and other military applications , 2004, SPIE Defense + Commercial Sensing.

[12]  Hae Young Noh,et al.  Robust Occupant Detection Through Step-Induced Floor Vibration by Incorporating Structural Characteristics , 2016 .