Weighted consensus algorithms for distributed localization in cooperative wireless networks

In this paper we consider distributed localization in a wireless peer-to-peer network. Each node is required to estimate the geographical configuration of the whole network (i.e., the positions of all nodes regardless of link availability) based on local processing and iterated information exchange with neighbors. We propose a new weighted-average consensus method based on received-signal-strength peer-to-peer measurements. To handle the non-linearity of the measurement model, an iterative Gauss-Newton location estimator is integrated into the consensus algorithm. Consensus processing is designed so as to account for the different degree of reliability of the location information provided by different neighbors and boost the convergence process. Performances are analysed and compared to those of conventional consensus algorithms, in terms of accuracy and convergence rate, using fundamental performance bounds as benchmarks.

[1]  Moe Z. Win,et al.  Ranging With Ultrawide Bandwidth Signals in Multipath Environments , 2009, Proceedings of the IEEE.

[2]  John E. Dennis,et al.  Numerical methods for unconstrained optimization and nonlinear equations , 1983, Prentice Hall series in computational mathematics.

[3]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[4]  Giuseppe Carlo Calafiore,et al.  A Distributed Technique for Localization of Agent Formations From Relative Range Measurements , 2012, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Richard M. Murray,et al.  DISTRIBUTED SENSOR FUSION USING DYNAMIC CONSENSUS , 2005 .

[6]  R.L. Moses,et al.  Locating the nodes: cooperative localization in wireless sensor networks , 2005, IEEE Signal Processing Magazine.

[7]  Santiago Zazo,et al.  Distributed Gauss-Newton method for localization in Ad-hoc networks , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[8]  Soummya Kar,et al.  Distributed Sensor Localization in Random Environments Using Minimal Number of Anchor Nodes , 2008, IEEE Transactions on Signal Processing.

[9]  Reza Olfati-Saber,et al.  Distributed Kalman filtering for sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[10]  Luc Vandendorpe,et al.  Bayesian Localization in Sensor Networks: Distributed Algorithm and Fundamental Limits , 2010, 2010 IEEE International Conference on Communications.

[11]  F. Gustafsson,et al.  Mobile positioning using wireless networks: possibilities and fundamental limitations based on available wireless network measurements , 2005, IEEE Signal Processing Magazine.

[12]  Umberto Spagnolini,et al.  Cooperative estimation for under-determined linear systems , 2014, 2014 48th Annual Conference on Information Sciences and Systems (CISS).

[13]  Anna Scaglione,et al.  Non-linear least squares estimation via network gossiping , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[14]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[15]  Monica Nicoli,et al.  A Bayesian Approach to Device-Free Localization: Modeling and Experimental Assessment , 2014, IEEE Journal of Selected Topics in Signal Processing.

[16]  Matti Latva-aho,et al.  Ultra-wide band sensor networks in oil and gas explorations , 2013, IEEE Communications Magazine.

[17]  Alejandro Ribeiro,et al.  Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals , 2008, IEEE Transactions on Signal Processing.

[18]  G.B. Giannakis,et al.  Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks , 2005, IEEE Signal Processing Magazine.

[19]  M. Nicoli,et al.  Fundamental Performance Limits of TOA-Based Cooperative Localization , 2009, 2009 IEEE International Conference on Communications Workshops.

[20]  Josip Pečarić,et al.  The arithmetic mean — the geometric mean and related matrix inequalities , 1997 .

[21]  Reza Olfati-Saber,et al.  Consensus and Cooperation in Networked Multi-Agent Systems , 2007, Proceedings of the IEEE.

[22]  Moe Z. Win,et al.  Cooperative Localization in Wireless Networks , 2009, Proceedings of the IEEE.

[23]  John W. Fisher,et al.  Nonparametric belief propagation for self-localization of sensor networks , 2005, IEEE Journal on Selected Areas in Communications.

[24]  Henk Wymeersch,et al.  Cooperative simultaneous localization and tracking (coslat) with reduced complexity and communication , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[25]  Monica Nicoli,et al.  Distributed estimation of macroscopic channel parameters in dense cooperative wireless networks , 2014, 2014 IEEE Wireless Communications and Networking Conference (WCNC).