Mobile Indoor Localization Systems

Despite the increasing interest in the area of mobile indoor localization, the positioning capability of Wireless Local Area Network (WLAN)-based mobile devices does not meet the goal of high accuracy and fast time response for scenarios with mobile devices (such as smartphones and wearables) as target device. We have designed and built an approach that uses Time-of-Flight (ToF) measurements and relies on software upgrades of simple commercial off-the-shelf (COTS) 802.11 chipsets with a customized firmware that can be integrated in any access point (AP). Our solution filters noisy measurements collected by WiFi chipsets of three dollars each. Our novel filtering technique needs just a few samples to estimate the distance range. The system has been tested across different and heterogeneous setups and testbeds (including scenarios with strong indoor multipath - the reception of reflected signals), resulting in a median error of the distance of 1.7 − 2.4 m [3].

The system has also participated in the Microsoft indoor localization competitions, that aims to bring together real-time or near real-time indoor location technologies and compare their performance in the same space [4,5]. In the 2016 competition in Vienna, Austria, our WiFi positioning system achieved 3.17m average error with only 5 COTS APs deployed and was ranked 5th out of 12 for the category based on Commercial Off-The-Shelf (COTS) devices.

Our system runs on commodity WiFi hardware and is the simplest solution available today to swiftly build a positioning system in a new indoor environment. Unless other systems, it does not require neither manual and costly offline pre-calibration nor any special hardware [3]. This avoids the problems of: 1) extensive manual on-site calibration, 2) requesting the collaboration from the mobile users through the installation of a dedicated applications on mobile devices when no necessary, 3) battery of the mobile device depleted faster because of the inertial sensors

We also investigate the scenario of a commodity mobile device, such as a smartphone or a laptop, trying to compute its own position. We have carried out a performance evaluation of self-positioning algorithms such as the least-squares and the extended Kalman filter as multilateration problem solvers and tested them over two ToF-based localization technologies, the Global Positioning System (GPS) and the aforementioned WiFi ToF localization system. In [6] we have shown how crucial is the adequate tuning of the measurement noise autocovariance matrix to obtain good performances with the EKF. If this is done correctly, the EKF outperforms the LS for GPS positioning while in the case of WiFi they achieve comparable accuracies in a static scenario as a result of the heterogeneous sources of noise of ToF GPS and WiFi.

References

  1. http://research.microsoft.com/en-us/events/msindoorloccompetition2016/default.aspx

  2. http://persys.networks.imdea.org/team

  3. Andreas Marcaletti, Maurizio Rea, Domenico Giustiniano, Vincent Lenders, Aymen Fakhreddine (December 2014) 
    Filtering Noisy 802.11 Time-of-Flight Ranging Measurements (Paper) [PDF  ]
    In: The 10th ACM International Conference on emerging Networking EXperiments and Technologies (ACM CoNEXT 2014), 2-5 December 2014, Sydney, Australia

  4. Dimitrios Lymberopoulos, Domenico Giustiniano, Vincent Lenders, Maurizio Rea, Andreas Marcaletti, - et al. (Microsoft Indoor localization Competition 2014) (April 2015) 
    A Realistic Evaluation and Comparison of Indoor Location Technologies: Experiences and Lessons Learned (Paper) [PDF  ]
    In: ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2015), 13-16 April 2015, Seattle, USA

  5. Maurizio Rea, Héctor Cordobés de la Calle, Domenico Giustiniano, Vincent Lenders (April 2016) 
    Robust WiFi Time-of-Flight Positioning System (Demo) [PDF  ]
    In: Microsoft Indoor Localization Competition - The 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (ACM/IEEE IPSN 2016), 11-14 April 2016, Vienna, Austria

  6. Aymen Fakhreddine, Domenico Giustiniano, Vincent Lenders (May 2016) 
    Evaluation of Self-Positioning Algorithms for Time-of-Flight based Localization (Paper) [PDF  ]
    In: The 14th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt 2016), 9-13 May 2016, Tempe, Arizona, USA