Spot Fi Decimeter Level Localization using Wi Fi

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Spot. Fi: Decimeter Level Localization using Wi. Fi Proposed by - Manikanta Kotaru, Kiran

Spot. Fi: Decimeter Level Localization using Wi. Fi Proposed by - Manikanta Kotaru, Kiran Joshi, Dinesh Bharadia, Sachin Katti Stanford University Presented by – Mohammad Nasim Imtiaz Khan and Abdullah Ash Saki

Applications of Indoor Localization Targeted Location Real Life Analytics Indoor Navigation Based Advertising (e.

Applications of Indoor Localization Targeted Location Real Life Analytics Indoor Navigation Based Advertising (e. g. Airport Terminals) (Gym, Office, etc. . ) Indoor localization platform providing decimeter-level accuracy could enable a host of applications 2

Requirement for Ideal Localization System

Requirement for Ideal Localization System

1. Easily Deployable • Commercial Wi. Fi chips 3

1. Easily Deployable • Commercial Wi. Fi chips 3

1. Easily Deployable • Commercial Wi. Fi chips • No hardware or firmware change

1. Easily Deployable • Commercial Wi. Fi chips • No hardware or firmware change 4

1. Easily Deployable • Commercial Wi. Fi chips • No hardware or firmware change

1. Easily Deployable • Commercial Wi. Fi chips • No hardware or firmware change • No User Intervention 5

2. Universal • Localize any Wi. Fi device • No specialized sensors 6

2. Universal • Localize any Wi. Fi device • No specialized sensors 6

3. Accurate • Error of few tens of centimeters 1 m 7

3. Accurate • Error of few tens of centimeters 1 m 7

State-of-the-art System Deployable Universal Accurate RADAR, Bahl et al, ’ 00 HORUS, Youssef et

State-of-the-art System Deployable Universal Accurate RADAR, Bahl et al, ’ 00 HORUS, Youssef et al, ’ 05 Array. Track, Xiong et al, ’ 13 Pin. Point, Joshi et al, ’ 13 CUPID, Sen et al, ’ 13 LTEye, Kumar et al, ’ 14 Phaser, Gjengset et al, ’ 14 Ubicarse, Kumar et al, ’ 14 9

State-of-the-art System Deployable Universal Accurate RADAR, Bahl et al, ’ 00 HORUS, Youssef et

State-of-the-art System Deployable Universal Accurate RADAR, Bahl et al, ’ 00 HORUS, Youssef et al, ’ 05 Array. Track, Xiong et al, ’ 13 Pin. Point, Joshi et al, ’ 13 CUPID, Sen et al, ’ 13 LTEye, Kumar et al, ’ 14 Phaser, Gjengset et al, ’ 14 Ubicarse, Kumar et al, ’ 14 Spot. Fi, Kotaru et al, ’ 15 10

System Overview 11

System Overview 11

Localization - Overview 12

Localization - Overview 12

Localization - Overview 13

Localization - Overview 13

Challenge - Multipath 14

Challenge - Multipath 14

Solving The Multipath Problem State-of-the-art Subcarriers Model signal on antennas alone Spot. Fi Model

Solving The Multipath Problem State-of-the-art Subcarriers Model signal on antennas alone Spot. Fi Model signal on both antennas and subcarriers Antennas 15

Step 1: Resolve Multipath 16

Step 1: Resolve Multipath 16

Signal Modeling Equal Distance Line 17

Signal Modeling Equal Distance Line 17

Phase 1 / frequency 0 Distance travelled by the Wi. Fi signal 18

Phase 1 / frequency 0 Distance travelled by the Wi. Fi signal 18

Signal Modeling – Ao. A (Angle of Arrival) Equal Phase Line 19

Signal Modeling – Ao. A (Angle of Arrival) Equal Phase Line 19

Signal Modeling - Ao. A 3 2 1 20

Signal Modeling - Ao. A 3 2 1 20

Say There Are Two Paths… 21

Say There Are Two Paths… 21

Say There Are Two Paths… 22

Say There Are Two Paths… 22

Say There Are Two Paths… 23

Say There Are Two Paths… 23

Problem Statement CSI - Known 24

Problem Statement CSI - Known 24

Problem Statement Parameters - Unknown 25

Problem Statement Parameters - Unknown 25

Problem Statement 26 Number of paths (or Ao. As) < Number of antennas (or

Problem Statement 26 Number of paths (or Ao. As) < Number of antennas (or equations)

Typical Indoor Multipath 27

Typical Indoor Multipath 27

That’s A Problem State-of-the-art Commodity Wi. Fi chips Number of antennas/equations should be at

That’s A Problem State-of-the-art Commodity Wi. Fi chips Number of antennas/equations should be at least 5 28

How To Obtain More Equations? Subcarriers Model signal on both antennas and subcarriers Antennas

How To Obtain More Equations? Subcarriers Model signal on both antennas and subcarriers Antennas 29

Each Subcarrier Gives New Equations 30

Each Subcarrier Gives New Equations 30

Signal Modeling – To. F (Time of Flight) 31

Signal Modeling – To. F (Time of Flight) 31

Estimate both Ao. A and To. F More number of equations in terms of

Estimate both Ao. A and To. F More number of equations in terms of parameter of our interest 32

Say There Are Two Paths… 33

Say There Are Two Paths… 33

Say There Are Two Paths… 34

Say There Are Two Paths… 34

Problem Statement 35 Subcarrier 2 Subcarrier 1 CSI - Known

Problem Statement 35 Subcarrier 2 Subcarrier 1 CSI - Known

Problem Statement 36 Subcarrier 2 Subcarrier 1 Parameters - Unknown

Problem Statement 36 Subcarrier 2 Subcarrier 1 Parameters - Unknown

37 Subcarrier 2 Number of equations = Number of Subcarriers x Number of Antennas

37 Subcarrier 2 Number of equations = Number of Subcarriers x Number of Antennas Subcarrier 1 Problem Statement

Ao. A, To. F Estimates 38

Ao. A, To. F Estimates 38

Step 2: Identify Direct Path 39

Step 2: Identify Direct Path 39

Ao. A, To. F Estimates 40

Ao. A, To. F Estimates 40

Use Multiple Packets 41

Use Multiple Packets 41

Use Multiple Packets 42

Use Multiple Packets 42

Use Multiple Packets 43

Use Multiple Packets 43

Use Multiple Packets 44

Use Multiple Packets 44

Direct Path Likelihood • Smaller To. F Higher weight Lower weight Higher weight 45

Direct Path Likelihood • Smaller To. F Higher weight Lower weight Higher weight 45

Direct Path Likelihood • Smaller To. F Lower weight • Tighter Cluster Higher weight

Direct Path Likelihood • Smaller To. F Lower weight • Tighter Cluster Higher weight Lower weight 46

Direct Path Likelihood • Smaller To. F Lower weight • Tighter Cluster Higher weight

Direct Path Likelihood • Smaller To. F Lower weight • Tighter Cluster Higher weight • More Packets Higher weight Lower weight 47

Highest Direct Path Likelihood 48

Highest Direct Path Likelihood 48

Step 3: Localize The Target 49

Step 3: Localize The Target 49

Use Multiple APs Direct Path Ao. A = 45 degrees Signal Strength = 10

Use Multiple APs Direct Path Ao. A = 45 degrees Signal Strength = 10 d. B Direct Path Ao. A = 10 degrees Signal Strength = 30 d. B 50 Direct Path Ao. A = -45 degrees Signal Strength = 20 d. B Find location that best explains the Ao. A and Signal Strength at all the APs

Use Different Weights Direct Path Ao. A = 45 degrees Signal Strength = 10

Use Different Weights Direct Path Ao. A = 45 degrees Signal Strength = 10 d. B Direct Path Likelihood Direct Path Ao. A = 10 degrees Signal Strength = 30 d. B Direct Path Likelihood 51 Direct Path Ao. A = -45 degrees Signal Strength = 20 d. B Direct Path Likelihood Use different weights for different APs

Evaluation 52

Evaluation 52

Testbed 40 m Target Access point 53 52 m AP Locations Target Locations

Testbed 40 m Target Access point 53 52 m AP Locations Target Locations

Indoor Office Deployment 10 m 40 m Ubicarse Spot. Fi 0. 3 m 0.

Indoor Office Deployment 10 m 40 m Ubicarse Spot. Fi 0. 3 m 0. 4 m Empirical CDF 16 m Array. Track 1 0, 8 0, 6 0. 4 m 0, 4 0, 2 0 0, 05 0, 5 5 Localization Error (m) 52 m Target Locations 54 AP Locations

Stress Test – Obstacles Blocking The Direct Path 40 m 52 m Target Locations

Stress Test – Obstacles Blocking The Direct Path 40 m 52 m Target Locations 55 AP Locations

Stress Test – Obstacles Blocking The Direct Path 40 m Empirical CDF 1 0,

Stress Test – Obstacles Blocking The Direct Path 40 m Empirical CDF 1 0, 8 0, 6 1. 3 m 0, 4 0, 2 0 0, 049999958532 Localization Error (m) 52 m Target Locations 56 AP Locations

Effect of Wi. Fi AP Deployment Density Empirical CDF 1 0, 8 0, 6

Effect of Wi. Fi AP Deployment Density Empirical CDF 1 0, 8 0, 6 3 APs 4 APs 5 APs 0, 4 0. 8 m 0, 2 0 0, 05 0, 5 5 Localization Error (m) 57

Conclusion • Deployable: Indoor Localization with commercial Wi. Fi chips • Accurate: Accuracy comparable

Conclusion • Deployable: Indoor Localization with commercial Wi. Fi 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 Wi. Fi chip 58

References • J. Xiong and K. Jamieson, “Arraytrack: A fine-grained indoor location system, ”

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, ” Mobi. Com ’ 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. Mc. Phillips, and K. Jamieson, “Phaser: Enabling phased array signal processing on commodity wifi access points, ” Mobi. Com ’ 14. • M. Youssef and A. Agrawala, “The horus wlan location determination system, ” Mobi. Sys ’ 05. • S. Sen, J. Lee, K. -H. Kim, and P. Congdon, “Avoiding multipath to revive inbuilding wifi localization, ” Mobi. Sys ’ 13. • K. Joshi, S. Hong, and S. Katti, “Pinpoint: localizing interfering radios, ” NSDI ’ 13. • M. Kotaru, K. Joshi, D. Bharadia, S. Katti, "Spot. Fi: Decimeter Level Localization Using Wi. Fi, " ACM SIGCOMM 2015. • All the icons are from the Noun Project https: //thenounproject. com/