Dynamic Model-Based Filtering for Mobile 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 mmcguire@dsp.toronto.edu Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.1/51
Outline 1. Signal Processing for Future Wireless Communications Systems. 2. Introduction to Mobile Terminal Location. 3. Zero Memory Estimation 4. Dynamic Estimation 5. Conclusions. 6. Future Work. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.2/51
Evolution of Wireless Services Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.3/51
Mobility & Multimedia Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.4/51
Mobility & Multimedia � UMTS (ETSI) 3G Systems IMT-2000 (ITU) Support user bit rates up to 2 Mbps High mobility environment: 144 kbps � IEEE 802.11 Ad Hoc Systems Bluetooth Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.5/51
Signal Processing for Wireless Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.6/51
Signal Processing for Wireless Key problems: Capacity Resource allocation Connection management Channel management Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.7/51
Signal Processing for Wireless Present: Reactive control methods Future: Proactive control methods Requires future system state estimation. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.8/51
State Estimation Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.9/51
State Estimation Adaptive estimation Learning model. Adapting to changing model. Estimation techniques Parametric Non-parametric Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.10/51
Mobility Management Need to know resources that terminals require in future Prediction of future locations. Channels Handoff algorithm Routing Power/Bandwidth allocation Power control Code selection (CDMA) Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.11/51
Mobility Management Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.12/51
Mobile Terminal Location Locating mobile terminal from radio signal Applications Resource allocation Location sensitive information Emergency communications Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.13/51
Terminal Location Methods Handset based Perception of user privacy. Currently greater accuracy. Network based Cheaper terminals. Greater potential accuracy FCC Requirements Configuration Accuracy Requirement > 67% > 95% Handset 50 m 150 m Network 100 m 300 m Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.14/51
Terminal Location Measurements Received Signal Strength(RSS), Time of Arrival (ToA), Time Difference of Arrival (TDoA). Angle of Arrival (AoA). Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.15/51
Terminal Location Measurements Measurement Type Advantages Disadvantages Received Signal Strength • low cost measurements • low accuracy in large cells (RSS) • simple computations Angle of Arrival (AoA) • simple computations • specialized antennae • low accuracy in large cells Time of Arrival (ToA) • time measurement re- • synchronized network re- quired for TDMA/CDMA quired • receiver must know time network operation • simple computations of transmission • expensive measurement Time Difference of Arrival • time measurement re- • synchronized network re- (TDoA) quired for TDMA/CDMA quired • expensive measurement network operation • receiver does not need • complex calculations time of transmission TDMA - Time Division Multiple Access, CDMA - Code Division Multiple Access Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.16/51
Radio Signal Measurements Non-linear effects make problem more complex Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.17/51
Radio Signal Measurements τ ( k ) is the vector of propagation time measurements for sample 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 ) = F τ ( k ) F is the measurement difference matrix. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.18/51
Geometric Dilution of Precision (GDOP) High Precision Geometry Low Precision Geometry Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.19/51
My Contribution 1. Improved Zero Memory Estimation 2. Bounds on Zero Memory Estimation Error 3. Model-based Dynamic Estimation New Filter Algorithm Developed 4. Bound on Dynamic Estimation Error Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.20/51
Zero Memory Estimation Previously proposed techniques are Maximum Likelihood Estimators(MLE). Problems with MLE: Prior knowledge is ignored. Assumed Line of Sight (LOS) propagation model. NLOS is common in urban areas of interest. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.21/51
Zero Memory Estimation Observations: Statistical knowledge of terminal position available from hand off algorithm. Propagation survey made during network configuration. = ⇒ Network has knowledge that can be used for location. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.22/51
Zero Memory Estimation k is sample interval. θ ( k ) is location of mobile terminal at k . ˆ θ ( k ) is estimated location of mobile terminal at k . Survey data: j survey point, j ∈ { 1 , 2 , ..., n } . θ j , location of survey point j . z j , measurement taken at survey point j . Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.23/51
Zero Memory Estimation Estimated location is weighted average of survey point locations: � n j =1 θ j h ( z ( k ) , z j ) ˆ θ ( k ) = � n j =1 h ( z ( k ) , z j ) h ( · ) is kernel function. Estimated location is weighted average of survey point locations. Weights determined by kernel functions. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.24/51
Zero Memory Bounds NLOS propagation creates discontinuities in propagation equations. Standard bounds (e.g. Cramer-Rao no longer apply). Use other bounds Barankin bounds Weinstein-Weiss bounds. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.25/51
Simulated Environment Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.26/51
Zero Memory Results Parametric MLE (TDoA) Parametric MLE (ToA) Non-parametric MAP (TDoA) 200 Non-parametric MAP (ToA) Parzen Gaussian (TDoA) Parzen Gaussian (ToA) 150 RMSE (m) 100 50 0 15 20 25 30 35 40 45 50 Standard Deviation of Range Error Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.27/51
Zero Memory Results 40 WWB ToA Simulated ToA 35 WWB TDoA Simulated TDoA 30 RMSE (m) 25 20 15 10 5 10 15 20 25 30 35 40 45 50 σ d (standard deviation of range error) Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.28/51
Dynamic Estimation Combine measurements from different sampling periods. Use dynamic model of mobile terminal motion. Dynamic model consists of: Kinematic model. Human Decision model. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.29/51
Mobile Terminal Motion Model Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.30/51
Kinematic Model x ( k ) is terminal state. u ( k ) is control input. w ( k ) is process noise. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.31/51
Human Decision Model Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.32/51
Zero Memory Estimator Preprocessor Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.33/51
Dynamic Estimation Prediction phase Correction phase Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.34/51
Dynamic Estimation Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.35/51
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. Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.36/51
Dynamic Estimation Results Fixed Control Input 16 Evaluation Bound Dynamic Filter 14 Zero Memory Estimator 12 RMSE (m) 10 8 6 4 2 0 20 40 60 80 Samples Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.37/51
Dynamic Estimation Results Changing Control Input 16 Evaluation Bound Dynamic Filter 14 Zero Memory Estimator 12 RMSE (m) 10 8 6 4 0 20 40 60 80 Samples Dynamic Model-Based Filtering for Mobile Terminal Location Estimation – p.38/51
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