1. Avoiding Multipath to Revive Inbuilding WiFi Localization
Mobisys ’13
Souvik Sen, Jeongkeun Lee, Kyu-Han Kim, Paul Congdon
Hewlett-Packard Labs
Taehoon Ha,
Multimedia and Mobile Communications Laboratory

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Outline
Introduction
CUPID : WiFi based Indoor Localization
Estimating Distance
Estimating Angle
Evaluation
Conclusion
This is outline. First I will explain Instruction and next introduce CUPID system that is WIFI base Indoor Localization. While introducing CUID system, I will explain how CUPID estimate distance and angle in detail.
Next I will show you result of evaluation. Lastly I will conclude this presentation
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Introduction
Current Indoor Location Technologies
Fingerprinting based
War-driving is expensive
Crowdsourcing is unattractive to user
WiFi based
Rely on distance or angle
Highly susceptible to indoor multipath
WiFi based approach is easier to adopt but
Needs to distinguish direct path from multipath reflections
Extensive interest in location-aware services has driven many novel indoor localization techniques.
The accuracy of fingerprinting based technologies come at the cost of war-driving. And such war-driving is not one time cost because environment can change due to change in layouts and objects, or something. To reduce the overhead of war-driving, crowdsourcing can be used. But they remain unattractive because a lack of clear user incentive to share sensor and location information.
Wifibased : it is posible to estimate distance using signal strenght(RSSI ), but RS냐 performs poorly because of multipath reflection
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Multipath Reflection
RSSI = Direct path energy + Reflected path energy
If we can accurately estimate the direct path, localization performance will improve significantly.
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5. CUPID:WiFi based Indoor Localization
Locates Clients using angle and distance
Captures distance and angle more accurately
Distance of direct path(Energy of direct path:EDP)
Angle of direct path(ANDP)
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6. Estimating EDP from CSI
Inverse Fast Fourier Transformation
Energy of Direct Path
푸리에변환: 시간영역의함수를주파수영역의함수로변환하는것.
By an appropriate Inverse Fast Fourier Transformation , the frequency domain C S I can be translated time-domain power-delay profile(PDF)
Energy of Reflected Path
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7. Estimating Distance from EDP
Estimate distance from EDP using the path loss equation
Path loss exponent can vary between 2 and 4, depending on environment
Incorrect path loss exponent an cause large errors in distance estimation
Needs to determine the correct path loss exponent
PR: EDP at AP
P0: Measured EDP
at a distance of 1 meter
γ : Path loss exponent
d : Distance
PR = P0 - 10γlog(d)
Gamma
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Path-loss exponent
Path loss exponent depends on the likelihood of line-of-sight
How can we determine line of sight?
This is CDF of Path-loss exponent from 500 known locations. Almost Path loss exponent is distributed between 2 and 3. 2 path los exponent means transmitter and receiver is in the line-of-sight environment like corridor, on the other hand for complex indoor scenarios the path loss can go upto 4. since rs냐 does not capture any information regarding wireless propagation, choosing the right path-loss exponent for each received packet is difficult.
Complex indoor environment
Line-of-sight environment
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Line-of-sight factor
LoS blockage may not affect reflected paths
Energy of direct path
RSSI = EDP + Reflected Energy
𝐿𝑜𝑆𝑓𝑎𝑐𝑡𝑜𝑟 𝑙𝑓𝑎𝑐𝑡𝑜𝑟 = 𝐸𝐷𝑃 𝑅𝑆𝑆𝐼
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10. Path-loss Exponent and lfactor
500 known locations, 1AP
Linear relationship between path loss exponent and the lfactor
5 APs
Relationship does not vary much over different environments
This graph shows the EDP path loss exponent with increasing lfactor for transmissions to a single. From these measurements we can apply linear fitting to establish a relationship between path loss exponent and the lfactor. Sencond graph show that the path loss vs. lfactor relationship may not depent on a particular AP, or event environment the line are similar because lfactor directly escimates the environmental factor affecting the EDP 쎄
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Estimating ANDP
Angle-of-arrival(AoA) estimation
AoA algorithm can identify multiple path and the output of MUSIC(AoA) is psudospectrum
∆𝑑= 𝜆/2sin⁡(𝜃) Δd
𝑝ℎ𝑎𝑠𝑒 𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 ∆𝜑 =2𝜋∆𝑑/𝜆
→ 𝜃=arcsin⁡(∆𝜑/ 𝜋)
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12. Angle Estimation Using Human Mobility
Strongest Peak may not ANDP because of LoS blockage
Then compare spectrum of different locations.
When weaker path is consistent, then ANDP can be approximated.
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13. AoA with dead reckoning
Compute the physical distance between the two location by consulting her phone’s accelerometer and gyroscope
Law of cosine
𝑐 2 = 𝑎 2 + 𝑏 2 −2𝑎𝑏𝑐𝑜𝑠𝛾
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14. AoA with dead reckoning
Determine angle of each
Find similar difference of two angle, then it is ANDP
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Side Ambiguity
A linear antenna array can differentiate between signals from one arrays side only
Identifies side by observing the user’s turn
3 antenna placed in straight line , can differentiate between signals from one arr
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16. Leveraging Multiple APs
Multiple Aps can estimate other Aps clients’ location by overhear transmission
estimates across them using a weighted centroid approach
di : distance
𝜃i : angle
APi,x , APi, y : location of AP
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CUPID Architecture
Overall Architecture
Changing Angle
New Angle
Distance
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Evaluation
Methodology
Client broadcasted 5 packets per second
Receive the last 2 seconds; 10 packets
Blue triangle is AP (5)
Walk around for an hour
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19. Distance estimation error
Different APs
Greatly reduces distance error
Different number of known location used to find lfactor-to-mapping
Even with a few known locations, perform well
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20. Angle estimation error
Different Aps
Median angle estimation accuracy is 20°
The error is mostly due to weak links.
Needs to exclude for better performance
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21. Performance using multiple APs
Increasing number of Aps
More APs, Smaller error
Exising RSSI and AoA schemes
Larger error than CUPID
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Conclusion
Multilateration and triangulation has traditionally suffered from the randomness of multipath reflections.
We find that CSI information can accurately estimate the distance, and angle from only the direct path.
CUPID identifies and harnesses only the direct path, avoiding the affect of multipath reflections.
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