RTLS Overview: Active RFID2010.ieee-rfid.org/files/2011/12/RTLS-session-Rich-Fletcher.pdf · •...
Transcript of RTLS Overview: Active RFID2010.ieee-rfid.org/files/2011/12/RTLS-session-Rich-Fletcher.pdf · •...
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RTLS Overview:Active RFID
Rich FletcherApril 2010
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Outline:
• Active RFID RTLS Overview• UWB vs Coherent• Technologies and Standards• Propagation Issues for coherent• Emerging Approaches for RTLS
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Common RFID Requirements
• Perfomance: accuracy• Low-Cost• Reliability• Long battery life• Long communication range (10m-100m)• Low data rate is OK• Anti-collision (TDMA, CSMA, etc) needed for applications with large tag populations• Conformance with Industry standards for applications that are not closed loop• optional: Mesh capability • Security has increasing importance
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Sample RTLS Architecture
Reader
REMOTESERVER
TAGS
TAGS
TAGS
TAGS
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Approach #1: “Discretized” RTLS
• Use multiple readers or beacons• Divide problem into read zones• Disadvantage: does not provide continuous Pos.
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“Discretized” RTLS Architecture
Locator Beacon
Reader
REMOTESERVER
Locator Beacon
Locator Beacon
Locator Beacon
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“Discretized” RTLS Architecture
Locator Beacon
Reader
REMOTESERVER
Locator Beacon
Locator Beacon
Locator Beacon
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Approach #2: “Out of Band” Sensors
• Use separate signal to do positioning• Examples: ultrasound, Infrared, other carrier• Disadvantage: requires sensor on tag
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“Out of Band” Sensor Positioning
Reader
REMOTESERVER
Signal Emitter
Signal Emitter
Signal Emitter
Signal Emitter
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Approach #3: “Pure” RTLS
• Use multiple readers• Use only available observable signals
Challenges:• Dynamic environments• Real-time?
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“Pure” RTLS Architecture
Reader
REMOTESERVER
TAGS
TAGS
TAGS
TAGS
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RTLS Observables
Reader
REMOTESERVER
TAGS
TAGS
TAGS
TAGS
Observables:Amplitude, Time, Phase
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The Challenge: sample data
• signals not monotonic with position
RSSI vs Distance
200
205
210
215
220
225
230
235
240
245
250
0 10 20 30 40
5 dBi ZT-100 Hor5 dBi ZT-100 Vert
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General Approach
OBSERVABLES
RTLS Model
Propagation model
Geometric model
Probability model
POSITION
Calibration Data
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Technologies and Standards
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Active RFID Categories
UWB
Coherent
READER
TAG
READER
TAG
Battery or other power source
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Ultra-Wide Band
UWB
Relevant observables: time, amplitudeChallenges: dispersion, RX sensitivity, regulation
READER
TAG
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IEEE 802.15.4a - UWB• Commercial Vendors (sample)
• Ubisense• Multi Spectral Solutions • Parco Wireless• Time Domain
• Advantages• Low power • Good data rate (but still less than 1Mbps)• resistant to interference • Precise positioning using TOA (10 cm accuracy)
• Disadvantages• Shorter range than narrowband frequency domain radios • Standardization efforts IEEE 802.15.4a and 802.15.3a (W-USB) have not been very successful • UWB Frequency standards vary around the world
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Coherent Transmission
Coherent(e.g. DS-SS)
READER
TAG
Battery or other power source
Relevant observables: amplitude (RSSI), phaseChallenges: multi-path
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“Coherent-Active” Standards
Several Competing Standards:
• ISO 18000-7 (Savi)
• IEEE 802.11 (WLAN)
• IEEE 802.15.4 (Zigbee, WPAN)
• (Bluetooth 802.5.1 not considered due to master-slave protocol model)
• Others (ISO)
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IEEE 802.11 (WLAN)• Advantages
• Popular, open and active standard • Long communication range• Robust security features • Existing access points and routers can be configured as readers • Native multi-hop capability will become available in 2008 when 802.11s standard is ratified• Spread spectrum physical layer• Emerging standard for Real Time Location (RTLS): ISO/IEC 24730-2
• Disadvantages • Higher cost• Higher power consumption in general• Impact of large tag population on Wi-Fi networks unclear(IP address allocation, 802.11a/b/g/n/s network impact)
• Still requires moving/installing access points/readers for RTLS
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ISO 18000-7 (Savi)• Advantages
• Designed specifically for active RFID (1990)• Dominant in military/govt market• Signals at 433 MHz have better penetration through dielectrics than higher frequencies• worldwide acceptance for 433 MHz• 32-bit UID plus user memory
• Disadvantages• Longer wavelength (λ=0.7m) – Not preferred for indoor use or metal enclosures• Low bandwidth – bit rate = 27.7 Kbps• very basic support for security (4 byte password)• Reader-Talks first communication, random time-slotted anti-collision• Requires License (Savi/Lockheed)
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IEEE 802.15.4 Tags• Advantages
• Designed specifically for low data rate embedded networks – new generation of radio technology• Very low power• Many low cost chipsets available:
Texas Instruments/ChipconAtmelST MicroElectronicsFreeScaleEmberNordics, etc.
• Support for reader mesh through ZigBee. • 27 channels available in 3 different frequency bands –868.3 (1), 906-926 (10) and 2405-2480(16) MHz• Integrated, high-security 128-bit AES Encryption• Ratified and active standard• Short wavelength (λ=12 cm at 2.4 GHz) – good for indoor use
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IEEE 802.15.4 Tags
• Operating modes:• Tag talk first – optimize battery life• Reader talk first – optimize tag density
• Disadvantages of 2.4 GHz 802.15.4• Frequency bands are crowded (RFID readers, microwave ovens, cordless phones, Bluetooth (802.15.1), WLAN) • Fragmented standards for Network Layer protocols
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2.4 GHz vs 433 MHzAdvantages of 433 MHz:• Better outdoor propagation• World-wide acceptance
Advantages of 2.4 GHz:• Better indoor propagation• 8X smaller antennas• Wide selection of reader
antennas• Worldwide acceptance• Higher bandwidth more data• Integration with other sensor
networking standards (Zigbee, mesh)
2.4 GHz is usually better choice
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Wi-Fi Tags 802.11 (WLAN)
Chip vendors: G2, Gain span, …
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ZT-100
IEEE 802.15.4 Tags
• 802.15.4 –based protocol• Integrated antenna• 4-8 sensor inputs• Real-time Clock, Data logging
ZT-10, ZT-50
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Readers
ZR-HUB
ZR-USB
• Linux-OS 266 MHz Geode processor• Open Software API• Wi-Fi + Ethernet interface
ZR-SD
ZR-PCMCIA
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RF Propagation Issues
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General Loss Mechanisms
READERTAG
OBJECT
ReflectionLoss
TotalPathLoss
SpreadingLoss
PropagationLoss
= + +
(1/ r2)
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Reflections
READER OBJECTTAG
While absorption/loss depends on ε”, reflection depends largely on ε’
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Multi-path Reflections
READER
TAG
METAL OBJECT
Leads to non-monotonic dependence of RSSI on position
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Interference/Diffraction
READER TAG
OPAQUE ITEM
OPAQUE ITEM
OPAQUE ITEM
Pallet
OPAQUE ITEM
OPAQUE ITEM
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Emerging Approaches for Improving RTLS
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General RTLS Approach
OBSERVABLES
RTLS Model
Propagation model
Geometric model
Probability model
POSITION
Calibration Data
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BetterRF Modelling
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General RTLS Approach
OBSERVABLES
RTLS Model
Propagation model
Geometric model
Probability model
POSITION
Calibration Data
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Maxwell’s Equations
tH
E∂∂
−=×∇ μ
εq
E =⋅∇tE
EJH i ∂∂
++=×∇ εσ )(
0=⋅∇ Hμ
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Wave Equation Form
tE
tE
E∂∂
−∂∂
=∇2
2 μεμσ
vvvrrr 2∇−⋅∇∇=×∇×∇ )(
Using vector identity:
Leads to Wave Equation form of Maxwell’s Equations:
tH
tH
H∂∂
−∂∂
=∇2
2 μεμσ
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Waves in lossy matter
EEjE μεωωμσ 22 −=∇
HHjH μεωωμσ 22 −=∇
( )trkjeHE ω−⋅∝rr
,
βαγ jjk +=≡
If we assume time-harmonic fields:
Then wave equation can be easily written as:
Non-zero but finite conductivity, σ, and dielectric loss
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AC Loss Inside materials:
Good conductors:
εωσ
εεδ
′=
′′′
≡tan
EjEJ εωσ ′+=
EjjJ )tan( δεω −′= 1
Good dielectrics:
1>>′εωσ /
EJ σ≈
1<<′εωσ /EjJ εω ′≈
εεε ′′−′= j
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∫∫∫ •−=•
S
a daHdtd
dsJ μσ
∫∫∫ •=•
Sop
daJdsH
21llm μστ =
Shielding Effects, Reflection
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Materials properties
RF Loss critically depends on composition
Salt added (.05M)
ε’
tanδ
No salt
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Reflection - Transmission
READERTAG
OBJECT
Reflection 1 Reflection 2
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Simulation: Reflection
0 0.05 0.1 0.15 0.2width m
0.2
0.4
0.6
0.8
1
noit celf eR
e’ = 12.75 = 0.01, 0.03, 0.1
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Simulation: Transmission
0 0.05 0.1 0.15 0.2width m
0.2
0.4
0.6
0.8
1noissi
msnarTt nei ciff eoC
e’ = 12.75 = 0.01, 0.03, 0.1
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BetterRF Signalling
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General RTLS Approach
OBSERVABLES
RTLS Model
Propagation model
Geometric model
Probability model
POSITION
Calibration Data
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More Observables
• CMOS RFICs are continuously improving and adding more integrated functionality• Using standard CMOS RFICs, can we generate more observables?• Examples: power modulation, phase jitter, etc.
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BetterProbability Models
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General RTLS Approach
OBSERVABLES
RTLS Model
Propagation model
Geometric model
Probability model
POSITION
Calibration Data
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Machine Learning
• Hidden Markov Models (HMM)
S1(tn)
S2(tn)
S3(tn)
S1(tn+1)
S2(tn+1)
S3(tn+1)
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Generating Probabilities
OBSERVABLES
RTLS Model
Propagation model
Geometric model
Probability model
P(x)
Calibration Data
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Bayesian Propagation
• Propagate the P0 (prior) to P1 (posterior)
P(x)
x
P(x)
x
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Probability Summary
• Exploit locality property of Markov model based on recent state• Use Bayesian process to evolve probability distribution
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Summary
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Conclusions• Many RTLS solutions exist for Active RFID• RF propagation issues still exist – particularly
for dynamic environments• Emerging research: • RF CMOS ICs are enabling new signalling• Powerful Machine Learning techniques and
cheap fast computers/microntrollers are enabling unprecedented performance for RTLS