Project #4: WiFi finger-print aided intertial navigation

Background

It is well known that GPS has problems in providing localization indoors. Many different techniques are being pursued to solve the problem. In this project we will investigate a combination of two technqiues. One being inertial navigation and the other WiFi finger-print.

Inertial navigation is based on the output of two sensors namely accelerometers and gyroscopes (inertial sensors). Each of these sensors provides readings in three dimensions x,y and z. For aircrafts and submarines this technology gives an accurate location for hours without any externa aid. However, for personal localization using smartphones the cost and sensor size requirements implies low sensor quality. This implies that pure dead reckoning (i.e. integrating speed estimates) have unacceptable large errors grows. I order to limit this error we will combine this method with WiFi fingerprint. An implementation of a inertial measurement system was made in the course two years ago and is found in [2].

WiFi fingerprint was first introduced in [8] and it is based on Wi-Fi technology. The system calculates the user's position by empirical methods based on comparison with previous received signal strength indicator (RSSI) measurements (fingerprints). The positioning process consists of two phases. First, during off-line (learning) phase a mobile device constructs a radio map by measuring the Wi-Fi RSSI from each reachable access point [9] in a large number of known positions. Then, during the online phase, proximity-based matching algorithms are used to infer the user's location by comparing the current observed signal strength with the pre-recorded during the first phase radio map [3]. One could also consider using other measurements in addition to RSSI e.g. magnetic field strength measurements, in order to improve the performance. A discussion on modeling of fingerprint-based positioning systems can be found in [10]. Topological map-building is discussed in [9] and [11]. Different position-inferring algorithms are discussed in [3]. Implementation issues on different set-ups can be found in [12], [13] and [14]. Results from the implementation of a localization system for the second floor of the Q2 building is found in [4].


Figure 1: Illustration of accelerometer and gyroscope signals from inertial sensors mounted on the foot of a person.

Figure 2: Illustration of smartphone measuring the signal strength (RSSI) to three access points.

Specification

Basic requirements

Implement a system which can be used to navigate first inside the course lab (B230,Q-10) and then the foyer outside the lab. The system should perform better than the WiFi-only based system implemented by group yellow 2011, see [1]. Determine the localization performance of the system and describe the most important factors.

Advanced requirements

Analyze the influence of varios algorithm parameters on the performance of the system.

Mid-term requirements

The android assignment completed (given during the android lecture). A system implementation according to the basic requirements but with all the processing running in matlab. The data used as input is collected with a smart-phone and used for off-line processing.

Literature

  1. Group Yellow 2011, "WiFi finger-print", project homepage .
  2. Lee S.W., Cheng S.Y, Hsu J.Y.J., Huang P., You C.W., "Emergency Care Management with Location-Aware Services, "Pervasive Health Conference and Workshops, 2006, vol., no., pp.1-6, Nov. 29 2006-Dec. 1 2006.
  3. Brunato M., Battiti R., "Statistical learning theory for location fingerprinting in wireless LANs", Computer Networks, vol. 47, pp. 825-845, Dec. 2004.
  4. Group Green 2010, "Localization for walking person" project homepage
  7. Liu H., Darabi H., Banerjee P., Liu J, "Survey of Wireless Indoor Positioning Techniques and Systems,"Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol.37, no.6, pp.1067-1080, Nov. 2007.
  8. Bahl P., Padmanabhan V.N., "RADAR: an in-building RF-based user location and tracking system,"INFOCOM 2000. Proceedings of Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE, vol.2, no., pp.775-784 vol.2, 2000.
  9. Shin H., Cha H., "Wi-Fi Fingerprint-Based Topological Map Building for Indoor User Tracking,"IEEE 16th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), vol., no., pp.105-113, 23-25 Aug. 2010.
  10. Kaemarungsi K., Krishnamurthy P., "Modeling of indoor positioning systems based on location fingerprinting,"INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, vol.2, no., pp. 1012- 1022 vol.2, 7-11 March 2004.
  11. Ocana M., Bergasa L.M., Sotelo M.A., Flores R., Lopez E., Barea R., "Training Method Improvements of a WiFi Navigation System Based on POMDP, "International Conference on Intelligent Robots and Systems, 2006 IEEE/RSJ, vol., no., pp.5259-5264, 9-15 Oct. 2006.
  12. Cypriani M., Lassabe F., Canalda P., Spies F., "Open Wireless Positioning System: A Wi-Fi-Based Indoor Positioning System, "Vehicular Technology Conference Fall (VTC 2009-Fall), 2009 IEEE 70th, vol., no., pp.1-5, 20-23 Sept 2009
  13. LaMarca A. et al. "Place lab: Device positioning using radio beacons in the wild," In Proceedings of International Conference on Pervasive Computing (Pervasive), June 2005.
  14. Taheri A., Singh A., Agu E., "Location fingerprinting on infrastructure 802.11 wireless local area networks (WLANs) using Locus," Fourth International IEEE Workshop on Wireless Local Networks, Nov 2004.