1. Underwater Localization with Time-Synchronization and Propagation Speed Uncertainties
Roee Diamant, Lutz Lampe

2. Outline Brief overview on underwater communication
Localization in the underwater acoustic channel
Suggested localization protocol
Future work

3. Motivations
Most underwater activities require underwater communications [1]
Cables are heavy, deployment is expensive. Solution: wireless information transmission through the ocean
Wireless communication:
Radio (30Hz-300Hz, very high attenuation)‏
Optical (short distances, pointing precision)‏
Large body of relevant applications [2]:
Ocean exploration
warning systems, pollution control
Military underwater surveillance
Underwater oil exploration

5. Challenges of UWAC Fast time-varying frequency-selective channel [3]
Large Doppler shift and Doppler spread
Power attenuation increase with frequency
Ambient noise decreases with frequency
Half duplex communication
Slow propagation speed
[3]
Limited signal
bandwidth

6. Example of measured channel responses
Sea trial

7. Challenges of UWAC Propagation delay T low throughput
Character RF
UWAC
Effect
Propagation delay T
low throughput
Transmission rate
~1MB
~1Kb
Error probabilities
~10-7
~10^-4
Low reliability
SNR
high
low
Substantial multiple access interference
~10^5 T

8. Localization in UWAC Networks:
GPS is only used by surface nodes [4]
Accurate attenuation models are hard to find [3]
Propagation speed is an unknown parameter [5]
Network nodes are not time-synchronized
Nodes permanently move in the channel
Joint time-synchronization and location estimation where the propagation speed is an unknown variable

9. Outline Suggested localization protocol
Brief overview on underwater communication
Localization in the underwater acoustic channel
Suggested localization protocol
Future work

10. System Model Network of L anchor nodes at known time-varying locations
At least one unlocalized node at time-varying location
Nodes are not time-synchronized such that
Unlocalized node has INS system to self evaluate its location
Self-evaluated locations are not accurate but are used to accurately measure
movements for a short period of time
Anchor node index
Location index
Location index
Node’s m time
Offset
Node’s l time
Skew

11. System Model (2) Propagation delay is
where is the unknown propagation speed within
The objective is to estimate at the end of a localization window with duration

12. Available Measurements
Anchor node
Unlocalized node
time
We assume that time-of-arrival (ToA) measurements are
affected by i.i.d white Gaussian noise

13. Problem Formulation
Given

14. Time-Synchronization Followed by Localization
Eq. (1) and (2) can be rearranged in a matrix form
where depend on the skew and offset of the unlocalized node relative to the anchor node
Using separate LS estimators we estimate the clock skew and offset of the unlocalized node relative to all anchor nodes
Next, we obtain the propagation delay at all points,

15. Localization Motion vectors can be represented as
We get the following Eq.

16. Localization (2)
Using simple manipulation we represent the localization problem in the matrix form
where depends on the elements of
Following [6], we use the rough estimator
to construct vector and its covariance matrix,
Next, we refine the rough estimator to get a weighted LS estimator

17. Localization (3)
Finally, we represent the inner connection between the variables of (related by the motion vectors) in
to estimate
Refinement step: use to construct
Then, is used instead of the rough LS estimator

18. Flow Chart Online measurements : ToA and INS Initial processing:
ToA noise mitigation, motion vectors
Time synchronization for each anchor:
Estimate skew and offset
Estimate propagation delay
Localization:
Rough LS estimator
Construct error covariance matrix
WLS estimator
Utilize inner connection, WLS estimator
Iterative
refinement

19. Simulation Results
Simulations were performed using 2 anchor nodes and an unlocalized node, moving at random speed and directions
To simulate errors, we added i.i.d Guassian noise to ToA measurements, and motion vectors
Anchor nodes offset and skew where generated as i.i.d Guassian noise
Results were compared to the simple multileteration method and a benchmark method (joint protocol) [6]. Reference methods are given nominal propagation speed of 1500m/sec

20. Localization Simulation Results
All nodes time-synchronized
Sound speed known
All nodes are time-synchronized
Sound speed is unknown

21. Localization Simulation Results (2)
All nodes not time-synchronized, sound speed is unknown

22. Trial Sea

23. Sea Trial
Four vessels: three anchors and one unlocalized node, each deployed transceiver at 10m depth
Vessels moved freely with ocean current
Achieved localization accuracy ~10m (compared with GPS positioning of the vessels)

24. Summary and future work
We suggested a heuristic algorithm for UWAL
The algorithm compensates time-synchronization and sound- speed uncertainties
Extension of this work will include formalization of the Cramér–Rao bound for the considered problem, propagation speed estimation using localization and results from the sea trial
Follow-up research will be continuous tracking of already localized nodes

25. Bibliography
[1] M.Chitre, S.Shahabodeen, and M.Stojanovic, “Underwater acoustic communications and networking: Recent advances and future challenges,” in Marine Technology Society Journal, vol. 42, no. 1,2008, pp.103–116
[2] W. Burdic, Underwater Acoustic System Analysis. Los Altos, CA, USA: Peninsula Publishing, 2002.
[3] Stojanovic and J. G. Proakis, Acoustic (underwater) Communications in Encyclopedia of Telecommunications.
Hoboken, NJ, USA: John Wiley and Sons, 2003
[4] Lee, P. Lee, S. Hong, and S. Kim, “Underwater navigation system based on inertial sensor and doppler velocity log using indirect feedback kalman filter,” in Journal of Offshore and Polar Engineering, vol. 15, no. 2, jun 2005, pp. 88–95
[5] Tan, R. Diamant, W. Seah, and M. Waldmeyer, “A survey of techniques and challenges in underwater
localization,” Accepted for Publication in the ACM Journal of Ocean Engineering
[6] J. Zheng and Y. Wu, “Localization and time synchronization in wireless sensor networks: A unified approach,” in
IEEE Asia Pacific Conf. on Circuits and Sys., Macao, China, Nov. 2008
[7] S. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory. Englewood Cliffs, NJ: Prentice-Hall, 1993.

26. Thank you!
Questions?
Roee Diamant, Lutz Lampe