Post on 11-Jan-2016
description
Toward Better Indoor Localization:Cooperative Localization and Estimation
Fusion
Gary Chan, Associate Professor
The Hong Kong University of Science and Technology
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Outline
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Indoor localization techniques Improving accuracy on current localization
infrastructure Cooperative (Peer-to-peer) localization
Collaborative mobile devices Simulation results
Estimation fusion Optimally combining multiple estimations Preliminary results
Conclusion
Indoor Localization Mobile device capabilities and penetration of
wireless access networks Many new types of mobile services become viable
Location based service (LBS) is with great commercial potential
Indoor LBS Find the closest restaurant, the best-buy-of-the-day of a
shop, etc. Better localization
Better service Better routing for correctness and bandwidth efficiency
LBS relies on accurate localization of client devices in order to provide high quality services
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Challenges of Indoor Localization Global Positioning System (GPS) only works
well outdoor Indoor environment
Complicated layout leads to complex fading, shadowing and interference, affecting its accuracy
Line-of-sight (LoS) not easily achievable indoor Requirements
High accuracy Computationally light-weighted Privacy Etc.
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Localization Techniques
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Measurements Distance-based (time of arrival, time difference of arrival,
received signal strength, etc.) Angle-based (Angle of arrival) Pattern-based Motion, velocity and direction Electromagnetic Etc.
Techniques Trilateration (for distances) Triangulation (for angles) Inertial navigation systems (INS) Fingerprinting Optimization etc.
Distance-based techniques
Measure distances among nodes and infrastructure nodes/landmarks
Use mathematical property to estimate lcoation, e.g. Trilateration Graph embedding
methods
L3
N
L1 L2
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N L1 L2 L3
N 0 r1 r2 r3
L1 r1 0 d1 d3
L2 r2 d1 0 d2
L3 r3 d3 d2 0L2
L3
L1
Nr1
r3
r2d3 d2
d1
embedding methods
trilateration
Pros and Cons
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Pros Simple No expensive hardware
Often requires clock synchronization to calculate distances Accuracy is prone to signal fluctuation and clock
synchronization
Angle-based Techniques
Measure angles between the node and landmarks
Use mathematical properties to estimate location, e.g. 2-angle triangulation
(angles measured at landmarks)
3-angle triangulation (angles measured at mobile node)
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2-angle triangulation3-angle triangulation
trilateration
Pros and Cons
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Pros Less sensitive to signal attenuation Simple calculation (transformable into
trilateration) Cons
Requires special hardware (directional antennas) to measure angles
Can be affected by reflections or multipaths
Pattern-based Techniques
Associate observed patterns with location
Training Phase Measure signal
patterns at reference points
Establish a mapping between them
Online Phase Observe pattern at
unknown position Compare with trained
data Estimate location10
BS3
BS1
BS2
Pros and Cons
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Pros Fast estimation (just a look up) Accurate (if the map is current)
Cons Time-consuming and labor-intensive training
phase Map has to be current; not adaptive to
environmental changes
Electro-Magnetic Tag Approach Technologies
Infrared (IR) tags Ultrasonic Radio Frequency Identification (RFID) UWB (Ultra-wide band) Etc.
Characteristics Higher accuracy due to shorter range Some require line-of-sight
This category of techniques may be part of a localization system and provides alternative references to improve accuracy
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Comparison of Tag SchemesScheme Technique Components Examples Pros Cons
RFID RSS or direct referencing with RFID tag positions
Active/passive RFID tags
1. SpotON 2. LANDMARC
1. Low cost 2. Easy to deploy 3. Insensitive to NLOS
1. High density 2. Require reader with intense signal output
Bluetooth
RSS from ISM band
Positioning server, Wireless AP, Bluetooth tags
Topaz 1. High accuracy Short distance (and hence high density)
Ultrasound
RSS + RTOF (Roundtrip Time of Flight)
Ultrasound transceiver
1. Cricket 2. SmartLOCUS
1. Improved accuracy on time measurement because of lower transmission speed than EM wave 2. Low cost
Sensitive to the shapes of surface and the density of the material
Ultra Wide Bandwidth (UWB)
Transmitting signal over multiple bands of frequency simultaneously
UWB tags 1. Ubisense system
2. Sappire Dart
1. Can be used close to other RF signals without interference 2. Low power consumption 3. Signals are easy to detect and filter
High system cost due to relatively new technology
Inertial Navigation System (INS) Key components
Motion sensor Rotation sensor Acceleration sensor Etc.
Characteristics Continuously compute location based on previous location and
sensor information No external references needed Accumulation of errors over time
Performance of INS largely depends on drift compensation scheme in order to reduce propagation error
Integral computation Computationally intensive and error-prone
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Factors of Inaccuracies
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Not all techniques are 100% accurate Signal fading or transient signal fluctuation Measurement noise or uncertainty Clock synchronization or inaccuracy Landmark density Accumulation of errors over time (for INS) Environmental changes (for
pattern-based/fingerprinting) Lack of updates or measurement granularity
Etc.
How to Achieve Higher Accuracy?
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Augment upon the existing infrastructure Providing a natural transition path toward higher
accuracy Cost-effective
No expensive hardware For populated areas
Cooperative (Peer-to-peer) estimation Mobiles help each other to achieve better
accuracy Multiple estimations
Estimation techniques do not have to be treated in isolation
Combining their estimations for better accuracy
Collaborative Localization Using Peer-to-Peer Technique
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Infrastructure and Mobile Noes
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Infrastructure Some landmarks or access points (APs) to provide
basic localization Due to deployment cost, the accuracy is not high
Mobile nodes Limited computational power, transmission range
and battery life High density over the infrastructure network Form a mobile ad-hoc network to better estimate
their locations Achieving better localization using
cooperative mobile nodes
The Localization Scheme: Local Estimation (1)
Construct a table of neighbors by varying a node’s transmission range Quantized Distance
Vector (QDV) construction
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1
2
3
4
52 15 24 3
QDV
Identifier
Distance Level
Location Estimation (2)
mISOMAP1. Collect QDVs from
neighbors2. Compile QDVs
locally3. Generate
embedding using Multi-dimensional Scaling (MDS)
21 2
65
4
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MDS
Infrastructure to Fix the Embedding Embedding transformation
Requires at least 3 references to “fix” the embedding
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2
65
4
312
65
4
3 1
2
65
4
31
2
6
5
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1
Reflection
Rotation Translation
Localization Spreads Like a Ripple from Landmarks
Starts with a landmark doing the local estimation, then spreads to its neighboring nodes
Nodes receiving location updates become references of others
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Combining Estimations from Different Landmarks Together
Map refinement Combines
several relative positions to generate an absolute position by minimizing:
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px : absolute positionpLi : relative position to BN idxLi : distance from BN i
Locations are Well Estimated
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Real positions Estimated positionsNormalized Average actual distance error = 0.2805
Normalized Average relative distance error = 0.24883
Summary
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A collaborative localization scheme Distance-based Improves the accuracy of infrastructure network
Only requires quantized distance measurement Robust to measurement noise
Only requires signal power control No special hardware requirement No global synchronization
Only involves neighbor communication Low power consumption
Fully distributed Supports network dynamics
Estimation Fusion
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Research Motivation Many indoor location techniques deployed
Wi-Fi, RFID, GPS, INS, etc. Locations are estimated in isolation Different level of errors
Due to measurement noise, base-station density, calibration accuracy, etc.
A handheld may have all these estimations at the same time
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Objective: Combine, or fuse, estimations to attain better localization accuracy
1. Characterization of estimation errors of different localization techniques
Angle of arrival (AOA) Time different of arrival (TDOA) Roundtrip time of flight (RTOF) Inertial Navigation System (INS)
2. Given errors, optimally combine them With efficient, simple and distributed algorithm With environmental or topological constraints
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Localization Error: Angle of Arrival (AOA) AOA: angle between BS and MS
: AOA : coordinates of base station i : coordinates of the mobile : measurement noise
),( ii yx
),( yx
),0(~ 2 N
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Estimation Error Variance of the estimation
Related to 2 factors Distance between mobile and BS Variance of measurement noise
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Close Match Between Simulation and Analysis
Number of BS = 6
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Estimation Error Decreases with Base Stations
= 10 degrees
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Estimation Error: Time Difference of Arrival (TDOA)
TDOA1. Get time difference from the mobile
to different base stations2. Draw hyperbola for every set of time
difference3. Obtain the intersection point as the
mobile location Time Difference:
: : : Distance
measurement noise :
Synchronization noise
1TTi 1DDi
),0(~ 2 Np
),(~ fcfcTric
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Estimation Error Decreases with Base Stations
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Roundtrip Time of Flight (RTOF) TDOA requires synchronization of clocks between base
stations and devices TROF does not require that System components:
Clock Base stations
• Noise assumed: Clock shift: )1,0(~U
Round Trip Time of Flight
RTT: Round Trip Time
Estimation Error
Error Analysis of Inertial Navigation System System components:
Gyroscope : measure orientation Accelerometer: measure acceleration
• Two noise assumed: Gyroscope: Accelerometer:
a
),0(~ 21 N
22,0 N
Error Analysis
Possible Estimation
Possible Estimation
Distance Error
Degree Error
Estimation Error w.r.t. the Accelerometer Error
Super-linear Increase in Estimation Error with the Duration of Using INS
Estimation Error is Similar to a Normal
Estimation Fusion Given a number of estimations with location uncertainties, how
to optimally combine them?
Estimation i: Xi ~ N( xi , sigmai), Yi ~ N( yi , sigmai) Find a coordinate minimizing the expected distances to all
these estimations67
Problem Formulation Objective function
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Optimal Solution Optimal solution is a point estimate:
Closed-form expressions Simple and efficient computation
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Estimator Error Decreases with the Number of Estimators
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Topological Constraint: Alley
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Topological Constraint: Wall
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Topological Constraint: Corner
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Topological Constraint: Room
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Conclusion Good indoor LBS depends on accurate estimation of
mobile location Many indoor techniques have been studied and
deployed Studied in isolation
Cooperative localization Augment on top of existing infrastructure Mobile peers exchange messages with each other to attain
better accuracy than infrastructure alone Estimation fusion
Optimally combining multiple estimations to attain better accuracy
Estimation uncertainty characterization Simple closed-form solutions to combine them
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Thank YouQ&A
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