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Paper Study

Paper Study. Properties of Indoor Received Signal Strength for WLAN Location Fingerprinting. Source: Kamol Kaemarungsi and Prashant Krishnamurthy Telecommunications Program, School of Information Sciences, University of Pittsburgh E-mail: kakst112,prashk@pitt.edu

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Paper Study

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  1. Paper Study Properties of Indoor Received Signal Strength for WLAN Location Fingerprinting Source: Kamol Kaemarungsi and Prashant Krishnamurthy Telecommunications Program, School of Information Sciences, University of Pittsburgh E-mail: kakst112,prashk@pitt.edu 0-7695-2208-4/04 2004 IEEE

  2. Introduction • GPS vs. WLAN Positioning • GPS does not work well for indoor area • WLAN Positioning is suitable • Implementing WLAN Positioning System • By AOA & TDOA • Angel of Arrival & Time Difference of Arrival • Need specialized hardware at MS (mobile station) • By Fingerprinting • Receive Signal Strength (RSS) is commonly used • NIC and existing APs can be reused easily

  3. Introduction • Location Fingerprinting Database • Must be constructed before positioning • Mapping each location to a fingerprint • Each fingerprint of location L is presented as a vector R • R = ( r1, r2, r3 … rN), where rK is the average RSS values from AP #K of total number N • Radio map contains all such RSS vectors for a grid of locations

  4. Introduction • How does it work • A MS obtains a sample RSS vector P = ( p1, p2, p3 … pN) • Calculate Euclidean signal distance between sample P and other fingerprints R in database • The location is then estimated to be L which the Euclidean signal distance is smallest • Something wrong • Error is occurred if L is not where the P was sampled in fact • We need to understand the statistical properties of the location fingerprint (RSS vector)

  5. Introduction • Knowing the properties of RSS • Enable to design better algorithm to classify measured RSS vector P to particular location • The distribution of RSS value, their standard deviation, temporal variation and correlations between multiple APs should be considered

  6. Properties of RSS • Multipath fading causes received signal to fluctuate around mean value at particular location • Signal is modeled by combined effect of large-scale and small-scale fading

  7. Properties of RSS • Large-scale fading • Signal attenuation • Signal travel over a distance and is absorbed by materials along the way to receiver • Predict the mean value of RSS • Small-scale fading • Dramatic fluctuation • Due to multipath fading • Usually modeled by • Rayleigh distribution if no line-of-sight component • Rician distribution if exist line-of-sight component

  8. Properties of RSS • Indoor Perspective • Effect of user’s presence on a RSS set (a set of RSS from single AP in fixed location) • Statistical properties of single RSS set • Properties of multiple RSS sets (from APs) • Compare the differences between the RSS fingerprints of two locations

  9. Properties of RSS • Effect of user’s presence on RSS • The user close to antenna affects mean value and spread of average RSS values • Resonance frequency of water is 2.4 GHz and human’s body consists of 70% water!

  10. Properties of RSS • Effects of user’s presence • User was presence in first hour (upper figure) • User leave in second hour (lower figure) • User’s body spread the range of RSS values!

  11. Properties of RSS • Effect of user’s orientation • The orientation of user caused a variation in RSS level up to 5 dBm • User’s orientation is crucial and should be included in computing location information • Fingerprint at the same location may lose one RSS value in the vector if the orientation of user changed

  12. Properties of RSS • Statistical properties of RSS • Distribution of RSS • Standard deviation of RSS • Stationarity of RSS

  13. Properties of RSS • Distribution of RSS • No user presence • log-normally distributed • mean value is predicable and follows a standardized path lose model • User Presence • Asymmetric (non-Gaussian, not normally) distributed • Means value and modes were different • Tend to be left-skewed • Hard to model and fit it to well-known distributions • Better to reduce the distribution to a mean value

  14. Properties of RSS • Standard deviations of RSS • Similar for signals from same AP with no user’s presence at particular location • Lower the received signal level is, the smaller degree of standard deviation and vice versa • High-level RSS values from the same AP at different locations may be difficult to distinguish for positioning purposes • Nearby locations might be easily identified if both have low RSS levels

  15. Properties of RSS • Standard deviations of RSS • Good positioning cases usually occurs with bad communication signals

  16. Properties of RSS • Stationarity of RSS • Apply to ergodic theorem • breaking the series of RSS measurements into separate pieces over different time intervals. • Random process is stationary if… • Mean and variance remain the same over time, and • Autocovariance function has same shape for each separate time-series

  17. Properties of RSS • Stationarity of RSS • RSS meets the conditions of stationary in 15 min • Distribution of RSS is time-independent in short-term • Test to first condition fails in one-day time series • RSS random process in this case is non-stationary • Due to the changing indoor environment • Assume stationarity over small time scale for modeling purposes

  18. Properties of RSS • Properties of multiple RSSs at a particular location • Independence of multiple RSSs • Interference from multiple APs • 802.11 MAC operates where a transmission is either not heard or is deferred if a competing transmission exists • The APs use the same frequency do not interfere with the reception to each other

  19. Properties of RSS • Properties of RSS at different locations

  20. Properties of RSS • Implications on positioning algorithm • RSS samples exhibits clustering • concentrated around the center of a cluster with the greatest frequency of occurrence • various approaches provide similar accuracy • location fingerprints can be simply represented by vectors of average RSSs • Model of location fingerprints • pattern classifiers

  21. Modeling of RSS • Assumptions • RSS features in fingerprint is normally-distributed and stationary over a small time scale • Standard deviation of each RSS features are constant and unique • The mean of the RSS can be used as the fingerprint as samples of the RSS vector exhibit clustering • All RSS features in each location fingerprint are mutually independent

  22. Modeling of RSS • Two location system • Consider only two APs and one AP in which the error probabilities are higher • Probability of returning incorrect location at 95% C.I. for two-location system:

  23. Modeling of RSS • Twenty-five location system • Result of Gaussian model could be either optimistic or pessimistic • Probability of returning incorrect location at 90% C.I. for twenty-five location system:

  24. Conclusion • A frame work was given by this paper • Now we know the basic knowledge of WLAN positioning • What’s the next • Find or design a better model (empirical and Gaussian model are not good enough!) • We need more information about location recognizers, or location classifiers

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