1. Wireless Body Area Network Case Study: UWB based Human Motion Tracking 1

2. Outline 2 Localization via UWB ToA estimation of UWB signal TOA based human motion tracking  System setup and problem formulation  Self-calibration method  Localization method  Summary and outlook

3. Localization via UWB based on: H. Arslan, Z. N. Chen, M.G. di Benedetto: Ultra Wideband Wireless Communication, Wiley 2006 3

4. 4 Localization via UWB UWB IR is good candidate for short-range and low-rate communication networks –Nodes operating on battery (autonomy) –High precision ranging capability Location information is derived from radio signals between the target node (agent) and reference nodes (anchors) Common radio based positioning systems can be categorized into –angle of arrival (direction of arrival), –signal-strength, and –time-based approaches

5. 5 Angle of Arrival (AOA) Measure the AOA of target node’s signal at different reference nodes –Antenna arrays required –AoA measurement based on phase (time) difference of received wave-front at different antennas In 2 dimensional space, 2 reference nodes are enough

6. 6 Cramer-Rao Lower Bound (CRLB) Lower bound on the variance of an unbiased estimator For a uniform linear antenna array with N antennas, with a separation distance d For θ = nπ, n {0,±1,…}, the CRLB diverges –Two dimensional antenna grid necessary Not suited for UWB positioning –More antennas => higher complexity and increased costs –Large number of multipaths => multidimensional search for maximum likelihood AoA estimation

7. 7 Received Signal Strength Path loss model relates distance between two nodes to energy loss In 2D: Distance estimates from 3 reference nodes are required for triangulation

8. 8 Cramer-Rao Lower Bound (CRLB) Dependence on channel characteristics Very sensitive to the estimation of channel parameters –E.g. path loss exponent (n p ), variance of log-normal shadowing (σ sh 2 ), i.e. UWB signal characteristic (huge bandwidth) is not exploited

9. 9 Time-Based Approaches (1) Measurements of the propagation delay between nodes –Two nodes (A and B) with common clock Node A sends time-stamped signal Node B receives delayed version and can estimate time of arrival (ToA) and also the distance by correlation with a template signal For single path and additive white Gaussian noise (AWGN) channel, the CRLB of is given by –B eff is the root mean square signal bandwidth for signal s(t) with Fourier transform S(f) UWB very beneficial here! However, node synchronization is an important assumption –Accuracy of clocks plays an important role

10. 10 Time-Based Approaches (2) N reference nodes (at positions r i ) with ToA estimates τ i do positioning via least squares minimization –The weights w i reflect reliability of ToA estimates –Method becomes optimal if ToA measurements are modeled as true ToAs plus independent Gaussian noise samples Main sources of error in realistic environment –Multipath propagation –Non-line-of-sight propagation (direct path is blocked) –Interference from other nodes or coexisting systems

11. ToA Estimation of UWB Signal 11

12. 12 Signal Model (1) The ultra-wideband channel between the transmitter (agent) and the receiver (anchor) The received signal at the anchor where is the transmit signal and is an AWGN with PSD

13. 13 Signal Model (2) The Anchor is located at and the agent is located at If the agent and the anchor are in a LOS The goal of ToA estimators is to estimate given

14. 14 ToA Estimator: Matched Filter Assumes that the first arriving path is the strongest path ToA estimate Low complexity ˣ In harsh propagation environments, the strongest received echo may not coincide with the first path

15. 15 ToA Estimator: De-convolution Extracts all the paths by de-convolving the transmitted signal from Algorithms: WRELAX [1], CLEAN [2] Let are the detected paths ToA estimate Can work even under harsh propagation environment ×Computational complexity [1] J. Li and R. Wu, “An Efficient Algorithm for Time Delay Estimation”, IEEE Tran. on Sig. Proc., Aug. 1998. [2] J. A. Högbom, “Aperture Synthesis with a Non-Regular Distribution of Interferometer Baselines”, Astron. and Astrophys. Suppl. 15, 417-426.

16. 16 ToA Estimator: Search-Back Window and Thresholding Trade-off between matched filter and de-convolution approaches MF based ToA estimator Search back for the first cross- correlation crossing witin the window ToA estimate The search window size ( W ) and the threshold ( V T ) has to be adapted to the channel characteristics

17. TOA based Human Motion Tracking 17

18. Wide area of applications –Rehabilitation –Animation –Sports Applications of Human Motion Tracking 18 Image copyrights apply

19. Body Motion Tracking: State of the Art Optical systems Provide reliable tracking ×Dedicated infrastructure and skilled operators required Inertial systems No LOS restriction and high sampling rate ×Prone to drift errors Magnetic systems Accurate and no LOS restriction ×Prone to interference from nearby ferromagnetic materials 19

20. UWB based Human Motion Tracking Systems Advantages –Low complexity –Possible reuse of existing communication centric UWB BAN Challenges –Multipath propagation and potential of NLOS conditions –Limited choice of fixed anchor node positions –Anchor location uncertainty Considering geometric constraints promises performance improvement 20

21. The system consists 3 types of nodes Agent: extremely low-cost and low- power transmit-only asynchronous node Anchor: Low-complexity node which forwards the received signal to the cluster head Cluster head: a computationally capable device that implements the motion tracking algorithm. (E.g. a smart phone) 21 System Setup Cluster head : Anchor : Agent

22. Synchronization Requirements Agents are transmit-only asynchronous nodes –Transmit beacon signals Clocks of the anchors are frequency synchronized –E.g.: connected via e-fiber, exchange pilot sequences In here, the anchors are connected to the cluster head via cable The communication link between the anchors and the agents is not considered 22

23. TOA based Range Measurements (1) Error free distance measurement between anchor n and agent m 23

24. TOA based Range Measurements (2) We consider only LOS measurements Range measurement between anchor n and agent m Ranging error model [Qi’03] Before calibration –Uknowns: Goal of calibration: estimate 24 [Qi’03]: Y. Qi, “Wireless geolocation in a non-line-of-sight environment,” Ph.D. dissertation, Princeton University, Dec. 2003.

25. Formulation of the Self-Calibration Problem The agent locations are not surveyed The range measurements can be gathered from a single moving agent Vector of N r N t range measurements : covariance matrix of the range measurements map the offsets to the right range measurement 25

26. ML Solution of the Self-Calibration Problem Jointly estimates all the unknown parameters Maximum likelihood (ML) estimator A non-convex optimization problem Relaxation to a SDP problem (convex problem) –Serves as a good initialization for the ML estimator 26 Defines reference coordinate system and central clock Accounts body imposed constraints

27. Performance of the Self-Calibration Method Number of anchor ( N r )= 6, Number of agents ( N t )= 30, σ range = 2 cm 27

28. Localization Phase Ranging offsets of the anchors are calibrated out Range measurement between anchor n and agent m Anchor locations are known up to some uncertainty –Fixed on the torso (mobile); Calibration error Localization method estimates the unknown location and offsets of the agents – Accounts the anchor location uncertainties 28

29. Localization Method: ML Solution The ML solution of the localization problem A non-convex optimization problem Relaxed to a SDP problem Refinement with ML estimator 29 Accounts body imposed constraints

30. Performance of the Localization Method N r = 6, N t = 30, σ anchor_error = 5 cm 30

31. Agent nodes transmit PN sequence 31 Demonstrator System Agents Anchors 1-bit ADC receiver board LANs

32. Summary TOA based localization systems benefit from the very wide bandwidth of UWB signals TOA based human motion tracking system is presented Low complexity requirements (Asynchronous agents, Frequency synchronous anchors) Calibration phase: estimate clock offsets and locations of the anchors Localization phase: estimate the locations of asynchronous agents 32

33. Outlook Real-time demonstration of the developed algorithms Localization accounting NLOS conditions –Propagation effect of the body Utilizing the captured movements to detect activity (e.g. walking, sitting and standing) Communication link between the anchors and the cluster head 33