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Localization
Localization
Where am I?
Navigation
Where do I go?
Tracking
Where is my stuff?
2
Terminology
Infrastructure
Transmitters/receivers at known locations
Mobile device
Receives/transmit signal from/to infrastructure
Dead Reckoning
Tracking using internal sensors
3
Triangulation
Time-of-Arrival (TOA)
Use speed of medium to calculate distance
Use intersection from multiple sources for location
Round-trip Time-of-Flight (RTOF)
Passive reflector: Radar/Sonar
Active reflector: processing delay limits accuracy
Limited by time synchronization
Requires Line-of-Sight
4 “Survey of Wireless Indoor Positioning Techniques and Systems”, Liu et al.
Triangulation
Phase-of-Arrival (POA)
Phase difference together with wavelentgh, yields distance
Signal’s wavelength must be larger than cube diagonal
Req. LOS
Angle/Direction-of-Arrival (A/DOA)
Req. LOS
5 “Survey of Wireless Indoor Positioning Techniques and Systems”, Liu et al.
“Sensor Network-Based Countersniper System” Gyula Simon, Miklós Maróti, Ákos Lédeczi, György Balogh, Branislav Kusy, András Nádas, Gábor Pap, János Sallai, and Ken Frampton, 2004
MICAZ nodes with DSP for sound processing
Nodes measure Time-of-Arrival
Data collected at central PC
Shooter position triangulated
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Experimental Setup
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Scene Analysis
“RADAR: An In-Building RF-based User Location and Tracking System” Paramvir Bahl and Venkata N. Padmanabhan
WIFI Fingerprinting
Use the known Access Point locations as landmarks
Empirical
Systematically collect RSS/SNR measurements throughout building
Mobile receiver does a k-nearest neighbor search through database
Analytical
Use signal propagation models to predict RSS/SNR at different locations
Accuracy: 2-3 meters
9
Fingerprinting
“Indoor Localization Without the Pain” Chintalapudi et al.
Problems
Empirical data requires detailed survey of RSS in the area
Signal strength varies over time due to people and object moving
Question:
Can we with only a couple of fix points and a comparatively smaller survey get similar results?
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Main Idea
Measure relative AP signal strength
Use Log Distance Path Lost Model to solve localization eq.
Measure relative location between AP and receiver
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Fingerprint Crowdsourcing
Mobile users upload RSS measurement vectors on-the-go
System improves over time
Solutions to the Log Distance Path Loss model can be scaled, translated, rotated and/or reflected versions of the true locations
Use known fix points to solve ambiguity and fix map
Accuracy: 2-4 meters
Worse than RADAR but less setup and maintenance
12
“FM-based Indoor Localization” Yin Chen, Dimitrios Lymberopoulos, Jie Liu, and Bodhi Priyantha
Thursday’s paper
Use FM radio stations for fingerprinting instead of WIFI
Room level accuracy
Fingerprint more stable over time
Less maintenance
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Reverse Scene Analysis
Use infrastructure to collect signals from mobile devices
Smartphones
WIFI
Bluetooth
Track customers movement when shopping
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GPS
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GPS (Simplified)
Location based on time-of-flight from (at least) 4 known satellite locations
Satellite transmissions include very accurate timestamp
Solve for unknown (x,y,z,t)
tB: receiver clock offset
16 Souce: http://www.math.tamu.edu/~dallen/physics/gps/gps.htm
GPS Hardware
32 satellites
Approximately 20,200 km altitude
64-89 milliseconds signal delay
1.575 GHz CDMA (Code Division Multiple Access)
Each satellite encodes signal with unique 1023 chip code
Signal
Timestamp
Almanac (valid for 180 days)
Coarse trajectory and status of all satellites
Ephemeris (valid for 4 hours)
Precise parameters for the (one) satellite’s orbit
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GPS Data
Data Packet
50 bits per second
Five 300 bits frames
Repeats every 30 second
Can deduce millisecond signal propagation
Timestamp every 6 second
Ephemeris every 30 second
Almanac every 12.5 minutes
18 Image: “Energy Efficient GPS Sensing with Cloud Offloading”, Liu et al.
GPS Signal Modulation
Data packet encoded with 1023 bit C/A code at 1023 kbps
C/A Code repeats every 1 ms
Final signal at 1.575 GHz
Can deduce nanosecond signal propagation
19 Image: “Energy Efficient GPS Sensing with Cloud Offloading”, Liu et al.
GPS Satellite Tracking
Doppler shift
800 m/s satellite has a 4.2 kHz Doppler shift
GPS receivers collects 25-40 frequency bins
Code Phase shift
Over sample 1023 bps code signal
8 MHz oversample ≈ 8000 bins
Satellite Tracking
Search frequency bins
Search code phase bins
20 Image: “Energy Efficient GPS Sensing with Cloud Offloading”, Liu et al.
GPS Power Consumption
17-40 mA
Comparable to the TelosB’s 18 mA
Signal reception time
Almanac: 12.5 min. worst case
Ephemeris: 30 seconds
Tracking multiple satellites is power consuming
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Reducing Reception Time
Assisted GPS
GPS Location Server
Logs almanac and ephemeris
Transmit data to GPS receiver over high(er) bandwidth link
GPS receiver uses almanac and ephemeris as starting point for satellite search and tracking
22 Image: All About Symbian
Reducing Reception Time and Processing
“Energy Efficient GPS Sensing with Cloud Offloading”
Jie Liu, Bodhi Priyantha, Ted Hart, Heitor S. Ramos, Antonio A.F. Loureiro. Sensys 2012.
Main idea
Time synchronize with millisecond accuracy using other means
Use elevation data to constrain search
Collect and store raw GPS samples for online processing
Reception time can be reduced to 2 milliseconds!
2 C/A Codes to guard against bit transitions in the Navigational Data
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CO-GPS
24
Meanwhile in 1992…
“The TIDGET - A Low Cost GPS Sensor for Tracking Applications” ION Satellite Division International Technical Meeting, Albuquerque, NM, Sept. 1992
TrackTag GPS
Store raw GPS measurements in FLASH
Reconstruct route offline
Differential GPS
Error sources:
Satellite clock
Receiver clock
Atmospheric distortions
Use known location to calculate more accurate satellite ranges
Accuracy: ~10 cm
26 Oklahoma State University, http://www2.ocgi.okstate.edu/gpstools/
Low-cost Differential GPS
“High-Accuracy Differential Tracking of Low-Cost GPS Receivers” Will Hedgecock, Miklos Maroti, Janos Sallai, Peter Volgyesi, Akos Ledecz
Bluetooth GPS dongles
Sub-meter accuracy
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Argos – Reverse GPS
Polar Orbiting Environmental Satellites
NOAA/Eumetsat
6 satellites
Altitude: 850 km
Orbit: 100 min/14.1 revs
Passes: min. 4/day
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Argos
Use Doppler shift to determine when satellite passes
Use first and last signal in each pass to constrain search
Accuracy:
250-1500 m (depending on radio conditions)
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Argos
Developed at JHU APL!
…back in 1983!
Today
22g Argos/GPS hybrid
GPS location
Argos communication
$3950
Duty-cycle
Every 5 days
5 GPS fixes per day
3 year lifetime Argos platform transmitter terminals, left to right: Early solar-powered
PPT (APL), 30- and 20-g Nano PPTs (Microwave Telemetry, Inc.),
and prototye solar-powered GPS/PPT (Microwave Telemetry, Inc.).
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Inertial Measurement Unit
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Dead Reckoning
Inertial Measurement Unit:
3-axis accelerometer
3-axis gyroscope
3-axis magnetometer
General
Use measurements to calculate speed and location
Pedestrians
Distance: linear with step duration
Use sensor location on body to infer motion
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Dead Reckoning
Problems
Sensors and ADCs have inaccuracies
Errors accumulate over time leading to larger and larger deviations
Solution
Hybrid systems with periodic known locations to calibrate IMU or reset position
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IMU and WIFI Fingerprinting
“Pedestrian Navigation in Harsh Environments using Wireless and Inertial Measurements” J. Prieto, S. Mazuelas, A. Bahillo, P. Fernandez, R. M. Lorenzo, and E. J. Abril
Combine WIFI RSS and TOA with IMU
Foot mounted IMU
Use the stationary position to set IMU in known state
Use Kalman filter to fusion location estimates
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Walk multiple laps on the same floor
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IMU and Particle Filtering “Hybrid Positioning System: Combining Angle-based Localization,
Pedestrian Dead Reckoning and Map Filtering” P. Kemppi, T. Rautiainen, V. Ranki, F. Belloni, J. Pajunen
Main idea: Indoor positioning with fixed beacons (exact location)
Signal loss: switch to dead reckoning (relative location)
Use particle distribution for most likely locations
Use indoor maps to filter out illegal positions
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Angle-of-Arrival
“Angle-based Indoor Positioning System for Open Indoor Environments” by F. Belloni, V. Ranki, A. Kainulainen, A. Richter
Mobile transmitters
Transmit known pattern, 5 packets per second
1 receiver – array of antennas (with dual polarity)
Switch antennas during reception of 1 packet
Sample phase (Time-of-Flight) and amplitude of received signals
Calculate Angle-of-Arrival
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Direction-of-Departure
Reverse the process
Mobile receivers with “known” height (z)
Stationary transmitters with known position and direction
Calculate Direction-of-Departure
Cons:
Only 2D localization
High θ-resolution => high z
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Accuracy
Two beacons mounted to 20m high ceiling
Covering 1300 m2
Accuracy: <2.4 m 50%, <3.4 m 90%
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Fusion Filter
Use exact localization to construct/update probability distribution
Use PDR to update each particles location
Use map to filter out illegal movement
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Results
PDR with map filtering
630 m route
1 anchor room
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Results
Hybrid experiment
270 m route
Cafeteria: No line-of-sight and no map filtering guidance
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Bonus:
Cameras
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Kinect 2.0
Time-of-flight
LEAP motion
Stereoscopic triangulation
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Schedule
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Week 1: Introduction and Hardware
Week 2: Embedded Programming
Week 3: Medium Access Control
Week 4: Link Estimation and Tree Routing
Week 5: IP Networking
Week 6: JHU Special feat. Doug Carlson
Week 7: (seminar, no lecture)
Week 8: Energy Management and Harvesting
Week 9: Review and Midterm
Week 10: Time Synchronization
Week 11: Localization
Week 12: TBD
Week 13: (seminar, no lecture)
Week 14: TBD
