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

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

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

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

Example of measured channel responses Sea trial

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

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

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

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

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

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

Problem Formulation Given

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,

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

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

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

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

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

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

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

Trial Sea

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)

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

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.

Thank you! Questions? Roee Diamant, Lutz Lampe roeed@ece.ubc.ca