Localization

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Localization

• Updated on 11/6/2018

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Location

• Source of wireless signals
• Wireless emitter
• Location of a mobile device
• Some devices, e.g., cell phones, are a proxy of a
  persons location
• Used to help derive the context and activity
  information
• Location based services
• Privacy problems

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Location

• Well studied topic (3,000 PhD theses??)
• Application dependent
• Research areas
• Technology
• Algorithms and data analysis
• Visualization
• Evaluation

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Representing Location Information

• Absolute
• Geographic coordinates (Lat 33.98333, Long
  -86.22444)
• Relative
• 1 block north of the main building
• Symbolic
• High-level description
• Home, bedroom, work

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Some outdoor applications
E-911
Bus view
Car Navigation
Child tracking
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Some indoor applications
Elder care
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No one size fits all!

• Accurate
• Low-cost
• Easy-to-deploy
• Ubiquitous
• Application needs determine technology

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Lots of technologies!
Ultrasound
Floor pressure
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Wireless Technologies for Localization
Name Effective Range Pros Cons
GSM 35km Long range Very low accuracy
LTE 30km-100km Long range Very low accuracy
Wi-Fi 50m-100m Readily available Medium range Low accuracy
Ultra Wideband 70m High accuracy High cost
Bluetooth 10m Readily Available Medium accuracy Short range
Ultrasound 6-9m High accuracy High cost, not scalable
RFID IR 1m Moderate to high accuracy Short range, Line-Of-Sight (LOS)
NFC lt4cm High accuracy Very short range
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Localization Techniques

• Range-based algorithms
• Range-free algorithms
• Fingerprinting

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Range Based Algorithms

• Rely on the distance (angle) measurement between
  nodes to estimate the target location
• Approaches
• Proximity
• Lateration
• Hyperbolic Lateration
• Angulation
• Distance estimates
• Time of Flight
• Signal Strength Attenuation

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Approach Proximity

• Simplest positioning technique
• Closeness to a reference point
• Based on loudness, physical contact, etc

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Approach Lateration

• Measure distance between device and reference
  points
• 3 reference points needed for 2D and 4 for 3D

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Approach Hyperbolic Lateration

• Time difference of arrival (TDOA)
• Signal restricted to a hyperbola

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Approach Angulation

• Angle of the signals
• Directional antennas are usually needed

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Distance Estimation

• Multiple the radio signal velocity and the travel
  time
• Time of arrival (TOA)
• Time difference of arrival (TDOA)
• Compute the attenuation of the emitted signal
  strength
• RSSI
• Problem Multipath fading

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Distance Estimation TOA

• Distance
• Based on one signals travelling time from target
  to measuring unit
• d vradio tradio
• Requirement
• Transmitters and receivers should be precisely
  synchronized
• Timestamp must be labeled in the transmitting
  signal

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Distance Estimation TDOA

• Distance
• Based on time signals travelling time from
  target to measuring unit
• d vradio vsound (tradio- tsound) / (vradio
  vsound))
• Requirement
• Transmitters and receivers should be precisely
  synchronized
• Timestamp must be labeled in the transmitting
  signal
• Line-Of-Sight (LOS) channel

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Distance Estimation RSSI

• Distance
• Based on radio propagation model
• Requirement
• Path loss exponent ? for a given environment is
  known

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Range Free Algorithms

• Rely on target objects proximity to anchor
  beacons with known positions
• Neighborhood single/multiple closest BS
• Hop-count anchor broadcast beacons containing
  its location and hop-count
• Area estimation

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Fingerprinting

• Mapping solution
• Address problems with multipath
• Better than modeling complex RF propagation
  pattern

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Fingerprinting Steps

• Step1
• Use war-driving to build up location fingerprints
  (i.e. location coordinates respective RSSI from
  nearby base stations)
• Step2
• Match online measurements with the closest a
  priori location fingerprints

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Fingerprinting Example
SSID (Name) BSSID (MAC address) Signal Strength (RSSI)
linksys 000F662A6100 18
starbucks 000FC8001513 15
newark wifi 000625987A0C 23
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Fingerprinting Features

• Easier than modeling
• Requires a dense site survey
• Usually better for symbolic localization
• Spatial differentiability
• Temporal stability

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Summary of Localization Techniques
Measurement Scheme Accuracy Special Requirement
Range-based TOA Moderate Synchronization, dense beacons
Range-based TDOA High Synchronization, LOS, dense beacons
Range-based AOA High Directional antenna
Range-based RSSI Moderate No
Range-free Neighborhood Low No
Area estimation Moderate Dense Beacons
Hop count Moderate Dense Beacons
Fingerprinting RSSI High No
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Localization Systems

• Distinguished by their underlying signaling
  system
• IR, RF, Ultrasonic, Vision, Audio, etc 13

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GPS

• Use 24 satellites
• TDOA
• Hyperbolic lateration
• Civilian GPS
• L1 (1575 MHZ)
• 10 meter acc.

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Active Bat

• Ultrasonic
• Time of flight of ultrasonic pings
• 3cm resolution

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Cricket

• Similar to Active Bat
• Decentralized compared to Active Bat

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Cricket vs Active Bat

• Privacy preserving
• Scaling
• Client costs

Active Bat Cricket
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RADAR

• WiFi-based localization
• Reduce need for new infrastructure
• Fingerprinting

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Place Lab

• Beacons in the wild
• WiFi, Bluetooth, GSM, etc
• Community authored databases
• API for a variety of platforms
• RightSPOT (MSR) FM towers

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Computer Vision

• Leverage existing infrastructure
• Requires significant communication and
  computational resources
• CCTV

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Performance Metrics

• Accuracy
• Mean distance error (RMSE)
• Precision
• Variation in accuracy over many trials (CDF of
  RMSE)
• Robustness
• Performance when signals are incomplete
• Cost
• Hardware, energy

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Performance Evaluation
System/ Solution Wireless Technologies Accuracy Precision Robustness Cost
Active Badge 1 IR 3cm 90 Poor Low
Cricket2 Ultrasound 5cm 90 Poor Medium
BeepBeep 3 Sound 4cm 95 Poor High
Virtual Compass 4 Bluetooth WiFi RSSI 3.19m 90 Good Medium
APIT 5 WiFi RSSI 0.4 radio range Medium Low
DV-Hop 6 WiFi RSSI 3.5m 90 Medium Low
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Performance Evaluation
System/ Solution Wireless Technologies Accuracy Precision Robustness Cost
Centroid 7 WiFi RSSI 3.5m 90 Good Low
Amorphous 8 WiFi RSSI 0.2 radio range Medium Low
RADAR 9 WiFi RSSI 5.9m 95 Good Low
Horus 10 Bluetooth WiFi RSSI 2.1m 90 Good Low
SurroudSense 11 WiFi RSSI 90 N/A Good High
Ekahau 12 WiFi RSSI 2m 50 Good Low
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E-V Loc Goal

• Find a specific persons accurate location based
  on his electronic identifier and visual image
• - Publication
• Boying Zhang, Jin Teng, Junda Zhu, Xinfeng Li,
  Dong Xuan, and Yuan F. Zheng, EV-Loc Integrating
  Electronic and Visual Signals for Accurate
  Localization, in ACM MobiHoc12.

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E-V Loc Problem Formulation

• Input a target objects electronic identifier
  EID, a set (in a short time span) of E Frames
  with clear EIDs and the corresponding V Frames
  with possibly vague VIDs
• Output the target objects accurate position
  together with its visual appearance VID

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E-V Loc Work Flow
Need more signal samples?
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E-V Loc Nature of Our Solution

• E-V matching
• Uses electronic and visual signals as target
  objects location descriptors in E frames and V
  frames
• Matches the corresponding E and V location
  descriptors using Hungarian algorithm

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E-V LocLocalizing with Distinct VIDs

• Best match problem between EIDs and VDs

EIDs
VIDs
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E-V Loc Incremental Hungarian algorithm

• Find the best match between the EIDs and VIDs in
  each pair of E and V frame
• Iteratively perform the matching until a
  threshold is satisfied
• The threshold is derived based on the variance
  model of EIDs and VIDs

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E-V LocLocalizing with Indistinct VIDs

• Multi-dimensional best match problem
• Between EIDs and VIDs
• Among VIDs

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E-V Loc Two-dimensional Hungarian Algorithm

• Finding correspondence between different VIDs in
  neighboring frames
• Based on the correspondence, generating a
  consistent set of VIDs in all frames
• Using incremental Hungarian algorithm to perform
  the match

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Flash-Loc Flashing Mobile Phones for Accurate
Indoor Localization
- Publication Fan Yang, Qiang Zhai, Guoxing Chen,
Adam C. Champion, Junda Zhu and Dong
Xuan, Flash-Loc Flashing Mobile Phones for
Accurate Indoor Localization, in Proc. of IEEE
International Conference on Computer
Communications (INFOCOM), April 2016.
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Outline

• Overview
• Flash-Loc Design
• Localization Integration
• Implementation and Evaluation
• Summary

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Overview

• Accurate, fast and reliable localization of a
  flash light source
• User ID is carried by light flashes to
  distinguish different users
• It can work with visible and invisible light
• It can be implemented with commercial
  off-the-shelf devices.

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Working Scenario
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Flash-Loc Methodology (1)

• Where Fast, accurate and reliable positioning of
  flash light source with calibrated cameras
• Flashes with abrupt brightness changes are clear
  visual indicators of users
• Light flashes travel long distances before
  diminishing in intensity, this system can work in
  a wide range of area

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Flash-Loc Methodology (2)

• Who Distinguishable ID is delivered via flash
  light pattern
• Flash light with controllable flash pattern
• Flash ID is physically carried on flash pattern

Who Where Object Localization
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Challenges

• Time requirement (Fast)
• Long time flash is irritating
• Real-time localization
• Complicated scenarios (Robust)
• Multiple users
• Noise flash flash by non-users, environmental
  reflection

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Flash-Loc Design(1)

• Flash Coding
• Users unique code assigned by server
• Variable-length shorter ID code for fewer users
• Recycled one ID code can be shared by
  non-concurrent users
• Circular non-circularly-equivalent code, no
  synchronization bits
• Ex1 01 and 10 are circularly equivalent
• Ex2 0 and 1, 011 and 010 are non circularly
  equivalent

Example code length lt 4, 6 codes are
available 0, 1, 01, 0001, 0011, 0111.
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Flash-Loc Design (2)

• Flash generation
• Flash pulse width modulation, because it doesnt
  require accurate device time control
• Flash decoding
• Sequential video image subtraction based flash
  detection.
• Consider practical issues noise, reflection

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Flash-Loc Design (3)

• Flash localization with calibrated cameras

Single camera
Multiple cameras
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Flash-Loc Workflow
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Localization Integration

• Sparse deployment and infrequent use of Flash-Loc
  improve accuracy of continuous localization
  system
• Flash-Loc integrated with fingerprinting and dead
  reckoning
• Flash-Loc acts as check points to calibrate
  fingerprinting and dead reckoning

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Implementation

• Commodity Camera D-LINK DCS-930L network camera
• Flash Light Source Nexus S mobile phone with
  Android 2.3
• Server Lenovo Y570 (Core-i5 CPU and 4GB RAM)
• OS Linux 12.04
• Flash Detection OpenCV Python 2.4.9
• Control Program Multi-thread Python

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Experiment Setup

• 50m x 10m lobby
• 3 cameras, 6m high, 2.5m spacing

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Flash-Loc Accuracy

• Localization error vs. distance

One camera
Two cameras
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Flash time

• Multiuser flash time vs. distance

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Localization Integration Accuracy

• Localization error vs. accumulated time

Flash-Loc
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Summary

• Flash light localization
• Adaptive-length flash coding
• Pulse width modulation (PWM) based flash
  generation
• Sequential video image subtraction based flash
  localization
• Localization integration improves accuracy

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References

• Roy Want, Andy Hopper, Veronica Falcao, and
  Jonathan Gibbons. The active badge location
  system. ACM Transactions on Information Systems,
  10(1)91102, 1992.
• N. B. Priyantha, A. Chakraborty, and H.
  Balakrishnan. The cricket locationsupportsystem.
  In Proc. of ACM MobiCom, pages 3243, 2000.
• C. Peng, G. Shen, Y. Zhang, Y. Li, and K. Tan.
  BeepBeep ahigh accuracy acoustic ranging system
  using COTS mobiledevices. In ACM SenSys, pages
  114, 2007.
• N. Banerjee, S. Agarwal, P. Bahl, R. Chandra, A.
  Wolman, and M. Corner.Virtual compass relative
  positioning to sense mobile social interactions.
  InPervasive, 2010.
• T. He, C. Huang, B. Blum, J. Stankovic, and T.
  Abdelzaher. Range-free localizationschemes for
  large scale sensor networks. In Proc. of ACM
  MobiCom,pages 8195, 2003.

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References

1. D. Niculescu and B. Nath. DV based positioning in
   ad hoc networks. Journalof Telecom. Systems,
   2003.
2. N. Bulusu, J. Heidemann, and D. Estrin. Gps-less
   low cost outdoor localizationfor very small
   devices. IEEE Personal Communications Magazine,
   7(5)2834,October 2000.
3. R. Nagpal. Organizing a global coordinate system
   from local information on anamorphous computer.
   In A.I. Memo 1666. MIT A.I. Laboratory, August
   1999.
4. P. Bahl and V. N. Padmanabhan. RADAR an
   in-building rf-based user locationand tracking
   system. In Proc. of IEEE INFOCOM, March 2000.
5. M. Youssef and A. Agrawala. The Horus WLAN
   location determination system.In Proc. of ACM
   MobiSys, June 2005.
6. M. Azizyan, I. Constandache, and R. Roy
   Choudhury. Surroundsense mobilephone
   localization via ambience fingerprinting. In
   Proc. of ACM MobiCom,2009.

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References

• http//www.ekahau.com/
• Shwetak N. Patel , Location in Pervasive
  Computing, University of Washington.