Dynamic Model-Based Filtering for Mobile Terminal Location ...mmcguire/images/slides.pdf · Network...
Transcript of Dynamic Model-Based Filtering for Mobile Terminal Location ...mmcguire/images/slides.pdf · Network...
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Dynamic Model-Based Filtering forMobile Terminal Location Estimation
Michael McGuire
Edward S. Rogers Department of Electrical & Computer Engineering
University of Toronto,
10 King’s College Road, Toronto, Ontario, Canada M5S 3G4
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Outline
1. Signal Processing for Future Wireless CommunicationsSystems.
2. Introduction to Mobile Terminal Location.
3. Zero Memory Estimation
4. Dynamic Estimation
5. Conclusions.
6. Future Work.
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Evolution of Wireless Services
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Mobility & Multimedia
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Mobility & Multimedia
3G Systems
{
UMTS (ETSI)IMT-2000 (ITU)
Support user bit rates up to 2 MbpsHigh mobility environment: 144 kbps
Ad Hoc Systems
{
IEEE 802.11Bluetooth
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Signal Processing for Wireless
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Signal Processing for Wireless
Key problems:CapacityResource allocationConnection managementChannel management
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Signal Processing for Wireless
Present: Reactive control methods
Future: Proactive control methodsRequires future system state estimation.
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State Estimation
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State Estimation
Adaptive estimationLearning model.Adapting to changing model.
Estimation techniquesParametricNon-parametric
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Mobility Management
Need to know resources that terminals require in futurePrediction of future locations.
ChannelsHandoff algorithmRouting
Power/Bandwidth allocationPower controlCode selection (CDMA)
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Mobility Management
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Mobile Terminal Location
Locating mobile terminal from radio signal
ApplicationsResource allocationLocation sensitive informationEmergency communications
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Terminal Location Methods
Handset basedPerception of user privacy.Currently greater accuracy.
Network basedCheaper terminals.Greater potential accuracy
FCC RequirementsConfiguration Accuracy Requirement
> 67% > 95%
Handset 50 m 150 mNetwork 100 m 300 m
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Terminal Location Measurements
Received Signal Strength(RSS),Time of Arrival (ToA),Time Difference of Arrival (TDoA).
Angle of Arrival (AoA).
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Terminal Location Measurements
Measurement Type Advantages Disadvantages
Received Signal Strength(RSS)
• low cost measurements• simple computations
• low accuracy in large cells
Angle of Arrival (AoA) • simple computations • specialized antennae• low accuracy in large cells
Time of Arrival (ToA) • time measurement re-quired for TDMA/CDMAnetwork operation
• simple computations
• synchronized network re-quired
• receiver must know timeof transmission
• expensive measurement
Time Difference of Arrival(TDoA)
• time measurement re-quired for TDMA/CDMAnetwork operation
• receiver does not needtime of transmission
• synchronized network re-quired
• expensive measurement• complex calculations
TDMA - Time Division Multiple Access, CDMA - Code Division Multiple Access
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Radio Signal Measurements
Non-linear effects make problem more complex
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Radio Signal Measurements
τ (k) is the vector of propagation time measurements forsample time k
τ (k) = d(k) + ε(k)
d(k) is the vector of propagation distances.ε(k) is the vector of measurement noise.
z(k) is ToA/TDoA measurement vector:
z(k) = Fτ (k)
F is the measurement difference matrix.
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Geometric Dilution of Precision (GDOP)
High Precision Geometry
Low Precision Geometry
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My Contribution
1. Improved Zero Memory Estimation
2. Bounds on Zero Memory Estimation Error
3. Model-based Dynamic EstimationNew Filter Algorithm Developed
4. Bound on Dynamic Estimation Error
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Zero Memory Estimation
Previously proposed techniques are MaximumLikelihood Estimators(MLE).
Problems with MLE:Prior knowledge is ignored.Assumed Line of Sight (LOS) propagation model.
NLOS is common in urban areas of interest.
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Zero Memory Estimation
Observations:Statistical knowledge of terminal position availablefrom hand off algorithm.Propagation survey made during networkconfiguration.
=⇒ Network has knowledge that can be used forlocation.
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Zero Memory Estimation
k is sample interval.
θ(k) is location of mobile terminal at k.
θ̂(k) is estimated location of mobile terminal at k.
Survey data: j survey point, j ∈ {1, 2, ..., n}.θj , location of survey point j.zj, measurement taken at survey point j.
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Zero Memory Estimation
Estimated location is weighted average of survey pointlocations:
θ̂(k) =
∑nj=1
θjh(z(k), zj)∑n
j=1h(z(k), zj)
h(·) is kernel function.Estimated location is weighted average of surveypoint locations.Weights determined by kernel functions.
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Zero Memory Bounds
NLOS propagation creates discontinuities inpropagation equations.
Standard bounds (e.g. Cramer-Rao no longer apply).
Use other boundsBarankin boundsWeinstein-Weiss bounds.
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Simulated Environment
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Zero Memory Results
0
50
100
150
200
15 20 25 30 35 40 45 50
RM
SE (
m)
Standard Deviation of Range Error
Parametric MLE (TDoA)Parametric MLE (ToA)
Non-parametric MAP (TDoA)Non-parametric MAP (ToA)
Parzen Gaussian (TDoA)Parzen Gaussian (ToA)
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Zero Memory Results
10 15 20 25 30 35 40 45 505
10
15
20
25
30
35
40
σd (standard deviation of range error)
RM
SE (
m)
WWB ToASimulated ToAWWB TDoASimulated TDoA
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Dynamic Estimation
Combine measurements from different samplingperiods.
Use dynamic model of mobile terminal motion.
Dynamic model consists of:Kinematic model.Human Decision model.
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Mobile Terminal Motion Model
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Kinematic Model
x(k) is terminal state.
u(k) is control input.
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Human Decision Model
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Zero Memory Estimator Preprocessor
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Dynamic Estimation
Prediction phase
Correction phase
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Dynamic Estimation
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Bounds on Dynamic Estimation
Combine following information sources:Zero Memory Estimator.Dynamic model for mobile terminal motion.Prior distribution for mobile terminal location.
Bound calculated on squared error.
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Dynamic Estimation Results
Fixed Control Input
0 20 40 60 802
4
6
8
10
12
14
16
Samples
RM
SE (
m)
Evaluation BoundDynamic FilterZero Memory Estimator
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Dynamic Estimation Results
Changing Control Input
0 20 40 60 80
4
6
8
10
12
14
16
Samples
RM
SE (
m)
Evaluation BoundDynamic FilterZero Memory Estimator
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Dynamic Filter Comparison
6
7
8
9
10
11
12
13
14
0 20 40 60 80 100
RM
SE (
m)
Samples
Zero memory estimatorSimple Kalman filter (Hellebrandt et al.)
Simple Kalman filterMulti-model filter
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Dynamic Filter Comparison
Changing Control Input
2
3
4
5
6
7
8
9
10
0 20 40 60 80 100
RM
SE (
m/s
)
Time (s)
Multi-model filterSimple Kalman Filter
Simple Kalman Filter (Hellebrandt et al.)
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Dynamic Filter Robustness
6
8
10
12
14
16
0 0.2 0.4 0.6 0.8 1
RM
SE (
m)
Pr(TURN)
multi-model filter, optimized for Pr(TURN)=0zero memory estimator
multi-model filter, optimized for Pr(TURN)=2/3best multi-model filter for Pr(TURN)
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Dynamic Filter Robustness
7
8
9
10
11
12
13
0 5 10 15 20
RM
SE (
m)
Maximum Mean Velocity
multi-model filter, optimized for C=2.5 m/s2
zero memory estimator
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Dynamic Filter Robustness
7.5
7.6
7.7
7.8
7.9
8
8.1
8.2
8.3
0.1 0.15 0.20.1
0.15
0.2
α
α (F
ilter
)
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Results
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 5 10 15 20 25 30 35 40 45
Prob
abili
ty
Distance Error (m)
Dynamic Filter EstimatorZero Memory Estimator
Configuration Accuracy (ToA σd = 15 m)
67% 95%
Zero Memory Estimator 12.17 m 21.16 m
Dynamic Estimator 7.12 m 14.28 m
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Conclusions
Use all information sources.
Model-based estimation gives accurate locationestimates.
Efficiently combines information from different timeperiods.
Estimation methods are robust.Zero memory estimator robust to changes innoise/propagation model.Dynamic estimator robust to changes in dynamicmodel.
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Future Work
1. Applications of mobile terminal location.
2. Long term motion models.
3. Data fusion.
4. Location of terminals in ad hoc networks.Location of terminals in hybrid networks.
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Applications
Resource allocation
Hand off algorithms
Many possibilities for collaboration.
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Long Term Motion Models
Current dynamic filter based on short term motionmodels.
Long term motion models will improve estimation.
Improve motion prediction.
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Data Fusion
Use data from multiple information sources.RSS is cheap with wealth of propagation data buthas large uncertainty.ToA/TDoA are expensive with low uncertainties.AoA requires special antennae and provides varyingaccuracy.
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Ad Hoc Networks
Examples: Bluetooth, IEEE 802.11
Terminal must be low cost.
Limited connectivity between terminals.
Hybrid networks
Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.50/51
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Final words
Large amount of work to be done.
Many applications of results.
Potential to develop new estimation and filteringalgorithms.
Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.51/51