Relaxation of Distributed Data Aggregation for Underwater Acoustic Sensor Networks

Abstract : This aspect of the project is concerned with coordinating the sensors in an under water acoustic network in order to collaboratively track an acoustic source. Measurements are taken at each sensor node, and in order to obtain the best accuracy, the measurements should be jointly processed or fused. This requires communication and coordination among the nodes. At the same time, underwater communication is notoriously challenging. Channel conditions change rapidly and high data-rate communications are generally not possible. Consequently, protocols and mechanisms must be used which can adapt to the timevarying and unreliable communication medium.

[1]  Bo Yang,et al.  Radio-Frequency Tomography for Passive Indoor Multitarget Tracking , 2013, IEEE Transactions on Mobile Computing.

[2]  Peter R. Oke,et al.  A deterministic formulation of the ensemble Kalman filter : an alternative to ensemble square root filters , 2008 .

[3]  Mark Coates,et al.  Distributed ensemble Kalman filtering , 2014, 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[4]  Petar M. Djuric,et al.  Likelihood consensus: Principles and application to distributed particle filtering , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[5]  Michael G. Rabbat,et al.  Broadcast Gossip Algorithms for Consensus on Strongly Connected Digraphs , 2012, IEEE Transactions on Signal Processing.

[6]  Jun Ye Yu,et al.  Distributed underwater acoustic source localization and tracking , 2013, 2013 Asilomar Conference on Signals, Systems and Computers.

[7]  Michael G. Rabbat,et al.  Distributed auxiliary particle filters using selective gossip , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Anand D. Sarwate,et al.  Broadcast Gossip Algorithms for Consensus , 2009, IEEE Transactions on Signal Processing.

[9]  A. Doucet,et al.  A Tutorial on Particle Filtering and Smoothing: Fifteen years later , 2008 .

[10]  Soummya Kar,et al.  Gossip Algorithms for Distributed Signal Processing , 2010, Proceedings of the IEEE.

[11]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[12]  Yingmin Jia,et al.  Consensus-Based Distributed Multiple Model UKF for Jump Markov Nonlinear Systems , 2012, IEEE Transactions on Automatic Control.

[13]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[14]  Mark Coates,et al.  Asynchronous distributed particle filter via decentralized evaluation of Gaussian products , 2010, 2010 13th International Conference on Information Fusion.

[15]  Stephen P. Boyd,et al.  Randomized gossip algorithms , 2006, IEEE Transactions on Information Theory.

[16]  Stergios I. Roumeliotis,et al.  Set-Membership Constrained Particle Filter: Distributed Adaptation for Sensor Networks , 2011, IEEE Transactions on Signal Processing.