1. SpotFi: Decimeter Level Localization using WiFi
Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, Sachin Katti
Stanford University
Hi All. Today I would like to present SpotFi, our wifi-based indoor localization system. This is a joint work with my colleagues Kiran and Dinesh and with my advisor Prof Sachin Katti at Stanford University.

2. Applications of Indoor Localization
Indoor Navigation
(e.g. Airport Terminals)
Real Life Analytics
(Gym, Office, etc..)
Such a platform could be used to enable a host of user-generated applications such as targeted advertising, where retailers can target mobile devices carried by shoppers as they walk down specific aisles.
It can be used for general purpose indoor navigation, like finding your departure gate in an airport, or finding your way around a new conference venue.
It can also be used to enable a whole host of real life analytics – providing you insight into how much time you spend at the gym, and even which pieces of equipment you spend the most time on.
These are only the tip of the iceberg – submeter indoor localization could enable a whole range of other applications.
Targeted Location Based Advertising
Indoor localization platform providing decimeter-level accuracy
could enable a host of applications

3. Easily Deployable Commercial WiFi chips
We ideally want our localization system to be easily deployable.
It should be as easy as taking existing WiFi infrastructure, adding a software update and the system should work.

4. Easily Deployable Commercial WiFi chips No hardware or firmware change
We do not want any hardware changes like adding additional antenas. 4

5. Easily Deployable Commercial WiFi chips No hardware or firmware change
No User Intervention
Also we cannot expect any physical intervention from the user like rotating the wifi device.
This is important because both hardware modification and user intervention are expensive propositions and poses a huge hindrance for wide spread usage of the system. 5

6. Easily Deployable, Universal
Localize any WiFi device
No specialized sensors
We want our system to be universal, by this I mean that the system should localize any device that has a wifi chip.
We do not want to rely on existence of any other capability like presence of accelerometers and gyroscopes

7. Easily Deployable, Universal, Accurate
Error of few tens of centimeters
And we want our system to be accurate.
For indoor localization in retail stores we discussed before, an error of few meters throws off by multiple aisles.
So, for aisle level localization, we need accuracy on the order of few tens of centimeters.
The best
1 m

8. ArrayTrack, Xiong et al, ’13
State-of-the-art
System
Deployable
Universal
Accurate
RADAR, Bahl et al, ’00  
HORUS, Youssef et al, ’05
ArrayTrack, Xiong et al, ’13
PinPoint, Joshi et al, ’13
CUPID, Sen et al, ’13
LTEye, Kumar et al, ’14
Phaser, Gjengset et al, ’14
Ubicarse, Kumar et al, ’14
Indoor localization using WiFi is a well-studied problem.
Over the past 15 years, hundreds of systems have been proposed to localize devices using WiFi signals.
However, surprisingly, there exists no system that satisfies all the above three requirements.
systems which achieve state-of-the-art localization accuracy are not easily deployable.
And the systems that are deployable fall short of sub-meter localization demanded by applications like location based advertising.
And here comes SpotFi - the first localization system that works with commercial off-the-shelf WiFi chips, with no hardware/firmware modifications, with simple localization targets and achieves decimeter level accuracy.

9. ArrayTrack, Xiong et al, ’13
State-of-the-art
System
Deployable
Universal
Accurate
RADAR, Bahl et al, ’00  
HORUS, Youssef et al, ’05
ArrayTrack, Xiong et al, ’13
PinPoint, Joshi et al, ’13
CUPID, Sen et al, ’13
LTEye, Kumar et al, ’14
Phaser, Gjengset et al, ’14
Ubicarse, Kumar et al, ’14
SpotFi, Kotaru et al, ’15
Indoor localization using WiFi is a well-studied problem.
Over the past 15 years, hundreds of systems have been proposed to localize devices using WiFi signals.
However, surprisingly, there exists no system that satisfies all the above three requirements.
systems which achieve state-of-the-art localization accuracy are not easily deployable.
And the systems that are deployable fall short of sub-meter localization demanded by applications like location based advertising.
And here comes SpotFi - the first localization system that works with commercial off-the-shelf WiFi chips, with no hardware/firmware modifications, with simple localization targets and achieves decimeter level accuracy.

10. System Overview
At the highest level,

11. Localization - Overview
the working of SpotFi or many other localization systems is simple.
The localization target transmits a WiFi signal.

12. Localization - Overview
All the access points which can hear the transmited signal try to identify the direction of the target using the signal they receive.
Once the direction of the target is known from multiple vantage points, the target can be localized.

13. Challenge - Multipath
All the access points which can hear the transmited signal try to identify the direction of the target using the signal they receive.
Once the direction of the target is known from multiple vantage points, the target can be localized.

14. Solving The Multipath Problem
State-of-the-art
Model signal on both antennas and subcarriers
SpotFi
Subcarriers
Antennas
𝒇 𝟏
𝒇 𝟐
𝒇 𝟑
𝒇 𝟒
Model signal on antennas alone
Traditionally, large antenna arrays with 6-8 antennas are used to successfully resolve all the multipath components.
SpotFi exploits the fact the in regular WiFi OFDM, transmission happens on multiple subcarriers.
For example, a 20 MHz WiFi transmission has as many as 48 subcarriers.
By exploiting modeling along this additional dimension, SpotFi accurately resolves multipath using small number of antennas which is typical of current WiFi deployments.
Let us explore the modeling of traditional systems and SpotFi further.

15. Step 1: Resolve Multipath
𝜽 𝟏
𝜽 𝟐
Step 1: Resolve Multipath

16. Signal Modeling Equal Distance Line
Today’s wifi Aps have multiple antennas.
The transmitted signal from the target travels different distances to reach different antennas on the access points.

17. Phase 1 / frequency Phase Distance travelled by the WiFi signal
And depending on the distance traveled, the transmitted WiFi signal goes through different phase shifts.
Distance travelled by the WiFi signal

18. Signal Modeling – AoA (Angle of Arrival)
Equal
Phase
Line
Thus the received signal at different antennas have different phase shifts.
These phase shifts depend upon the direction from which the signal is arriving.

19. Signal Modeling - AoA Define Φ 1 = e − 𝑗2𝜋𝑑sin 𝜃 1 𝑐 𝑓
Phase at the antenna 1: 𝑥 1 = Γ 1
Phase at the antenna 2: 𝑥 2 = Γ 1 Φ 1
Phase at the antenna 3: 𝑥 3 = Γ 1 Φ 1 2
𝜽 𝟏
For example, here the signal is arriving at an Angle of Arrival or AoA theta1.
Then the phase of the received signal at the three antennas is given by equations highlighted in the box.
Gamma1 is the complex attenuation of the signal along the path. And phi1 is a quantity that depends on the angle of arrival theta1 .
I have used phi_1 to simplify the notation but observe that if we know phi_1, the AoA theta_1 can be obtained easily by substitution. 3 2 1
Γ 1 is complex attenuation of the path.
Φ 1 depends on AoA

20. Say There Are Two Paths…
Say there are two paths as shown in this example.

21. Say There Are Two Paths…
𝑥 1 = Γ 1
𝑥 2 = Γ 1 Φ 1
𝑥 3 = Γ 1 Φ 1 2
the first path introduces a vector of phase shifts at the three antennas depending upon the particular path’s attenuation and its Angle of arrival.

22. Say There Are Two Paths…
𝑥 1 = Γ Γ 2
𝑥 2 = Γ 1 Φ Γ 2 Φ 2
𝑥 3 = Γ 1 Φ Γ 2 Φ 2 2
The second path introduces its own vector of phase shifts. And the overall phase at each antenna is nothing but a combination of the phases introduced due to each of the paths.

23. Problem Statement 𝑥 1 = Γ 1 + Γ 2 𝑥 2 = Γ 1 Φ 1 + Γ 2 Φ 2
𝑥 1 = Γ Γ 2
𝑥 2 = Γ 1 Φ Γ 2 Φ 2
𝑥 3 = Γ 1 Φ Γ 2 Φ 2 2
The second path introduces its own vector of phase shifts. And the overall phase at each antenna is nothing but a combination of the phases introduced due to each of the paths.
Although these are a set of non-linear equations, we can use standard algorithms to solve these equations to obtain the directions of all the paths.
However, for these algorithms to work successfully the number of equations should be greater than the number of paths.
So, bottom line is number of multipath componments one can resolve is being limited by the number of antennas.
CSI - Known

24. Problem Statement 𝑥 1 = Γ 1 + Γ 2 𝑥 2 = Γ 1 Φ 1 + Γ 2 Φ 2
𝑥 1 = Γ Γ 2
𝑥 2 = Γ 1 Φ Γ 2 Φ 2
𝑥 3 = Γ 1 Φ Γ 2 Φ 2 2
The second path introduces its own vector of phase shifts. And the overall phase at each antenna is nothing but a combination of the phases introduced due to each of the paths.
Although these are a set of non-linear equations, we can use standard algorithms to solve these equations to obtain the directions of all the paths.
However, for these algorithms to work successfully the number of equations should be greater than the number of paths.
So, bottom line is number of multipath componments one can resolve is being limited by the number of antennas.
Parameters - Unknown

25. Number of paths (or AoAs) < Number of antennas (or equations)
Problem Statement
𝑥 1 = Γ Γ 2
𝑥 2 = Γ 1 Φ Γ 2 Φ 2
𝑥 3 = Γ 1 Φ Γ 2 Φ 2 2
The second path introduces its own vector of phase shifts. And the overall phase at each antenna is nothing but a combination of the phases introduced due to each of the paths.
Although these are a set of non-linear equations, we can use standard algorithms to solve these equations to obtain the directions of all the paths.
However, for these algorithms to work successfully the number of equations should be greater than the number of paths.
So, bottom line is number of multipath componments one can resolve is being limited by the number of antennas.
Number of paths (or AoAs) < Number of antennas (or equations)

26. Typical Indoor Multipath
All the access points which can hear the transmited signal try to identify the direction of the target using the signal they receive.
Once the direction of the target is known from multiple vantage points, the target can be localized.

27. Number of antennas/equations should be atleast 5
That’s A Problem
State-of-the-art
Commodity WiFi chips
In typical indoor environment, there are 5-8 significant multipath compoenents. So we need atleast those many antennas for resolving the multipath.
This is precisely the reason why previous localization systems required as many as 6-8 antennas to achieve decimeter level accuracy.
However, Standard off-the-shelf commercial WiFi chips come with upto three antennas only.
How can we obtain number of equations more than the number of paths when we have only three antennas?
Number of antennas/equations should be atleast 5

28. How To Obtain More Equations?
Model signal on both antennas and subcarriers
Subcarriers
Antennas
𝒇 𝟏
𝒇 𝟐
𝒇 𝟑
𝒇 𝟒
Key insight is to exploit another dimension – subcarriers
WiFi uses 48 subcarriers each with different frequencies
And each subcarrier has different phase depending upon the distance travelled and provides us wuth an additional equation.

29. Each Subcarrier Gives New Equations
𝒇 𝟏
𝒇 𝟐
SpotFi exploits the fact that, In WiFi, the signals are transmitted on multiple subcarriers each with different frequency using OFDM.
The key is that multipath also constrains phase of the received signal on each subcarrier in addition to constraining the phase on each antenna.
Depending upon the distance traveled by the path, signal on different subcarriers undergoes different phase shifts.
For example, here there are two subcarriers with frequencies f1 and f2.

30. Signal Modeling – ToF (Time of Flight)
Define Ω 1 = e −𝑗2𝜋 𝑓 2 − 𝑓 1 𝜏 1
Phase at first subcarrier: 𝑥 1 = Γ 1
Phase at second subcarrier: 𝑥 2 = Γ 1 Ω 1
ToF 𝝉 𝟏
And Time of flight tau is the time taken for the signal to reach the ap.
Then the phase of the received signal on different subcarriers is given by equations highlighted in the box.
Alpha1 is the complex attenuation of the signal along the path. And omega1 is a quantity that depends on the time of flight and frequency different between the subcarriers.
If we know omega1, we know time of flight.
We note that Time of flight for different paths is different.
Γ 1 is complex attenuation of the path.
Ω 1 depends on incoming signal ToF

31. Estimate both AoA and ToF
So, SpotFi’s approach is counter intuitive. We propose to estimate both Time of flight and angle of arrival for each path, instead of finding angle of arrival alone.
More number of equations in terms of parameter of our interest

32. Say There Are Two Paths…
At first subcarrier, for 3 antennas
𝑥 1 = Γ 1
𝑥 2 = Γ 1 Φ 1
𝑥 3 = Γ 1 Φ 1 2
At second subcarrier, for 3 antennas
𝑦 1 = Γ 1 Ω 1
𝑦 2 = Γ 1 Φ 1 Ω 1
𝑦 3 = Γ 1 Φ 1 2 Ω 1
For example consider the three canonical paths we did before. We model the phase of the received signal at each antenna-subcarrier pair instead of modeling the received signal on antennas alone.
And we model the received signal using both AoA and ToF instead of modeling it using AoA alone.
This results in the vector of phase shifts displayed on the right hand side.
So, although we now have more parameters to estimate, we also obtained many more equations.

33. Say There Are Two Paths…
At first subcarrier, for 3 antennas
𝑥 1 = Γ Γ 2
𝑥 2 = Γ 1 Φ Γ 2 Φ 2
𝑥 3 = Γ 1 Φ Γ 2 Φ 2 2
At second subcarrier, for 3 antennas
𝑦 1 = Γ 1 Ω Γ 2
𝑦 2 = Γ 1 Φ 1 Ω Γ 2 Φ 2 Ω 2
𝑦 3 = Γ 1 Φ 1 2 Ω Γ 2 Φ 2 2 Ω 2
As before, each multipath component introduces its own vector of phase shifts and the total received signal at each antenna-subcarrier pair is nothing but a linear combination of the received signal due to each path.

34. Problem Statement CSI - Known 𝑥 1 = Γ 1 + Γ 2 𝑥 2 = Γ 1 Φ 1 + Γ 2 Φ 2
𝑥 1 = Γ Γ 2
𝑥 2 = Γ 1 Φ Γ 2 Φ 2
𝑥 3 = Γ 1 Φ Γ 2 Φ 2 2
𝑦 1 = Γ 1 Ω Γ 2
𝑦 2 = Γ 1 Φ 1 Ω Γ 2 Φ 2 Ω 2
𝑦 3 = Γ 1 Φ 1 2 Ω Γ 2 Φ 2 2 Ω 2
CSI - Known
Subcarrier 1
Although we now have more paarmeters to estimate, we have many more equations.
Subcarrier 2

35. Problem Statement Parameters - Unknown 𝑦 1 = Γ 1 + Γ 2
𝑦 1 = Γ Γ 2
𝑦 2 = Γ 1 Φ Γ 2 Φ 2
𝑦 3 = Γ 1 Φ Γ 2 Φ 2 2
𝑦 1 = Γ 1 Ω Γ 2
𝑦 2 = Γ 1 Φ 1 Ω Γ 2 Φ 2 Ω 2
𝑦 3 = Γ 1 Φ 1 2 Ω Γ 2 Φ 2 2 Ω 2
Parameters - Unknown
Subcarrier 1
Although we now have more paarmeters to estimate, we have many more equations.
Subcarrier 2

36. Problem Statement 𝑥 1 = Γ 1 + Γ 2 𝑥 2 = Γ 1 Φ 1 + Γ 2 Φ 2
𝑥 1 = Γ Γ 2
𝑥 2 = Γ 1 Φ Γ 2 Φ 2
𝑥 3 = Γ 1 Φ Γ 2 Φ 2 2
𝑦 1 = Γ 1 Ω Γ 2
𝑦 2 = Γ 1 Φ 1 Ω Γ 2 Φ 2 Ω 2
𝑦 3 = Γ 1 Φ 1 2 Ω Γ 2 Φ 2 2 Ω 2
Number of equations =
Number of Subcarriers x
Number of Antennas
Subcarrier 1
Although we now have more paarmeters to estimate, we have many more equations.
Subcarrier 2

37. AoA, ToF Estimates
𝜽 𝟏 , 𝝉 𝟏
𝜽 𝟐 , 𝝉 𝟐
So, using standard tools, SpotFi now obtains the angle of arrival and time of flight of all the multipath components.
For example, here theta1 and tau1 represent the angle of arrival and time of flight estimates for the first path.

38. Step 2: Identify Direct Path
𝜽 𝟏 , 𝝉 𝟏
𝜽 𝟐 , 𝝉 𝟐
𝜽 𝟏 , 𝝉 𝟏
Step 2: Identify Direct Path

39. AoA, ToF Estimates
𝜽 𝟏 , 𝝉 𝟏
𝜽 𝟐 , 𝝉 𝟐
The next step is to identify which of the paths correspond to the direct path.
This is because direct path contains information about the direction of the target.
Notice that the direct path travels the shortest distance and has the smallest ToF value.
Well, one can use this to identify the direct path.
However, the direct path may not even exist or is greatly attenuated, for example, due to a obstacle like concrete pillar. In those scenarios, we do not want to consider the AoA measurements reported from the particular AP because, using AoA of a reflected path results in large localization errors.
So, how can we identify if a direct path exists and if it exists, how can we distinguish it from the other reflected paths?
SpotFi exploits the fact that typically multiple wifi packets are transmitted by the target.
Building upon the empirical observations from previous localization systems, SpotFi uses measurements from multiple packets to identify the direct path.

40. Use Multiple Packets 𝜽 𝟏 , 𝝉 𝟏 𝜽 𝟐 , 𝝉 𝟐
𝜽 𝟏 , 𝝉 𝟏
𝜽 𝟐 , 𝝉 𝟐
When the wireless environment changes slightly, the estimates of the AoA and ToF change slightly too.
So, the parameter estimates vary for all the paths from packet to packet.

41. Use Multiple Packets
For example, here I display the AoA and ToF estimates obtained for 170 packets in one of our experiments.
I have normalized the values so that both the axes are on same scale.

42. Use Multiple Packets
And the horizontal line represents the direction of the target

43. Use Multiple Packets
We can clearly notice that the AoA and ToF parameters form different distinguishable clusters, one cluster for each path.
This is because when the wireless environment changes, the estimates for each path change only slightly between different packets.
It has been empirically shown in previous systems, that the direct path parameters form a tighter cluster, that is they show much less varianmce than those corresponding to the other paths.
This can be clearly seen in this figure where the direct path cluster is smaller in its size than the cluster for any other path.
So we combine our observations of direct path having the smallest tof and direct path parameters having the smallest variance to help us identify the direct path if one exists. Specifically, we assign a direct path likelihood value for each path depending on three parameters.

44. Direct Path Likelihood
Smaller ToF
Higher weight
Higher weight
Lower weight
One, time of flight value - lower the ToF the more likely it is direct path.
Lower weight
Higher weight

45. Direct Path Likelihood
Smaller ToF
Tighter Cluster
Lower weight
Higher weight
Lower weight
Two - tightness of cluster - tighter the cluster the more likely it is direct path
Lower weight
Lower weight

46. Direct Path Likelihood
Smaller ToF
Tighter Cluster
More Packets
Lower weight
Higher weight
Higher weight
Three, number of points - the more the number of points, the more likely it is direct path rather than a spurious or anamoly path
Lower weight
Lower weight

47. Highest Direct Path Likelihood
Three, number of points - the more the number of points, the more likely it is direct path rather than a spurious or anamoly path

48. Step 3: Localize The Target
𝜽 𝟏 , 𝝉 𝟏
𝜽 𝟐 , 𝝉 𝟐
𝜽 𝟏 , 𝝉 𝟏
Step 3: Localize The Target

49. Find location that best explains the AoA and Signal Strength
Use Multiple APs
Direct Path AoA = 45 degrees
Signal Strength = 10 dB
Direct Path AoA = -45 degrees
Signal Strength = 20 dB
So each AP provides us with direct path AoA measurements which reveal the direction of the target
And they also provide the signal strength measurements which act as proxy for the distance of the target from the AP.
So, SpotFi finds the location that best explains the AoA and signal strength values at all the APs
Direct Path AoA = 10 degrees
Signal Strength = 30 dB
Find location that best explains the AoA and Signal Strength
at all the APs

50. Use Different Weights Direct Path AoA = 45 degrees
Signal Strength = 10 dB
Direct Path Likelihood
Direct Path AoA = -45 degrees
Signal Strength = 20 dB
Direct Path Likelihood
So each AP provides us with direct path AoA measurements which reveal the direction of the target
And they also provide the signal strength measurements which act as proxy for the distance of the target from the AP.
So, SpotFi finds the location that best explains the AoA and signal strength values at all the APs
Direct Path AoA = 10 degrees
Signal Strength = 30 dB
Direct Path Likelihood
Use different weights
for different APs

51. Evaluation
We refer the audience to our paper for a detailed description of SpotFi’s algorithm which localizes WiFi device using Channel State Information measurements.
From the design description, it is clear that the sysem works with commercial wifi chips and does not impose any restrictions on the target.
I would like to conclude by showing that SpotFi holds up the promise of required accuracy in real world deployments.

52. Testbed 40 m Access point Target 52 m Target Locations AP Locations
Specifically, we deployed SpotFi across almost an entire floor of our building.
Here is the floor plan of the building where we deployed SpotFi.
The access point are denoted by the red squares and the targets by blue dots.
The access points are Intel NUC based and use Intel 5300 WiFi chip with 3 antennas.
The target is a WiFi client mobile on a cart and has a single antenna.
The experiments are conducted using 40 MHz bandwidth in 5GHz band.
For each target location shown in the testbed, we placed our mobile client and collected CSI measurements from six of surrounding Aps.
We further compared our system with practical implementation of Arraytrack with three antennas.
Target
52 m
AP Locations
Target Locations

53. Indoor Office Deployment
ArrayTrack
Ubicarse
SpotFi
0.3 m
0.4 m
52 m
40 m
16 m
10 m
0.4 m
Localization accuracy is dependent on the multipath environment,
the material used in walls, the presence of metallic
objects, the density of WiFi AP deployment and many other
factors. Hence we start first by replicating the deployment
scenario used in evaluating previous state-of-the-art systems.
Specifically we used an indoor office environment with an area of roughly 16x10 sq.m . We deployed five-six APs to span the area.
The environment is very multipath rich but the targets typically have 4–5 Aps with a sufficiently strong direct path.
Our testbed targets for this experiment are highlighted.
SpotFi achieves a median localization error of 0:4 m.
To put these numbers in context, state-of-the-art systems used six-8 antennas or used a rotating antenna to achieve cm median accuracy.
To the best of our knowledge, no other localization system that works only with information that is already exposed by commodity WiFi cards and with no war-driving can achieve even sub-meter accuracy.
AP Locations
Target Locations

54. Stress Test – Obstacles Blocking The Direct Path
40 m
We now stress-tested our system by considering target locations which have only two or less number of Aps with strong direct path and in corridor scenarios.
For example, this particular target sees one AP in line of sight but the rest of the Aps are behind obstacles like walls and pillars.
SpotFi achieves a median accuracy of 1.3m.
The improved performance of SpotFi is due to two factors.
First, AoA estimation algorithm SpotFi uses is much better than the algorithm modeling antennas alone.
And second, SpotFi’s unique direct path likelihood estimates result in less weight for measurements from APs which do not have strong direct path to the target
This reduces the inaccuracy in location estimation.
52 m
AP Locations
Target Locations

55. Stress Test – Obstacles Blocking The Direct Path
1.3 m
40 m
We now stress-tested our system by considering target locations which have only two or less number of Aps with strong direct path and in corridor scenarios.
For example, this particular target sees one AP in line of sight but the rest of the Aps are behind obstacles like walls and pillars.
SpotFi achieves a median accuracy of 1.3m.
The improved performance of SpotFi is due to two factors.
First, AoA estimation algorithm SpotFi uses is much better than the algorithm modeling antennas alone.
And second, SpotFi’s unique direct path likelihood estimates result in less weight for measurements from APs which do not have strong direct path to the target
This reduces the inaccuracy in location estimation.
52 m
AP Locations
Target Locations

56. Effect of WiFi AP Deployment Density
We further evaluated SpotFi in scenarios with different access point densities
We varied the number of APs that can hear the target between three and
five to demonstrate the performance of SpotFi with increasing deployment.
As expected, accuracy
improves with increasing number of Aps.
Further, We observed that, even with four APs, SpotFi outperforms practical implementation of ArrayTrack with six Aps

57. Conclusion Deployable: Indoor Localization with commercial WiFi chips
Accurate: Accuracy comparable to state-of-the-art localization systems which are not suitable for wide deployments
Universal: Simple localization targets with only a WiFi chip
To conclude, I presented SpotFi, an accurate indoor localization system for any WiFi device.
SpotFi does not require specialized infrastructure.
And I hope that with all these useful properties, SpotFi ignites the indoor localization applications.

58. References
J. Xiong and K. Jamieson, “Arraytrack: A fine-grained indoor location system,” NSDI ’13.
S. Kumar, S. Gil, D. Katabi, and D. Rus, “Accurate indoor localization with zero start-up cost,” MobiCom ’14.
P. Bahl and V. N. Padmanabhan, “Radar: An in-building rf-based user location and tracking system,” INFOCOM 2000.
S. Kumar, E. Hamed, D. Katabi, and L. Erran Li, “Lte radio analytics made easy and accessible,” SIGCOMM ’14.
J. Gjengset, J. Xiong, G. McPhillips, and K. Jamieson, “Phaser: Enabling phased array signal processing on commodity wifi access points,” MobiCom ’14.
M. Youssef and A. Agrawala, “The horus wlan location determination system,” MobiSys ’05.
S. Sen, J. Lee, K.-H. Kim, and P. Congdon, “Avoiding multipath to revive inbuilding wifi localization,” MobiSys ’13.
K. Joshi, S. Hong, and S. Katti, “Pinpoint: localizing interfering radios,” NSDI ’13.
M. Kotaru, K. Joshi, D. Bharadia, S. Katti, "SpotFi: Decimeter Level Localization Using WiFi," ACM SIGCOMM
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