Distributed target localization using quantized received signal strength

In this paper, we propose a distributed gradient algorithm for received signal strength based target localization using only quantized data. The Maximum Likelihood of the Quantized RSS is derived and Particle Swarm Optimization is used to provide an initial estimate for the gradient algorithm. A practical quantization threshold designer is presented for RSS data. To derive a distributed algorithm using only the quantized signal, the local estimate at each node is also quantized. The RSS measurements and the local estimate at each sensor node are quantized in different ways. By using a quantization elimination scheme, a quantized distributed gradient method is proposed. In the distributed algorithm, the quantization noise in the local estimate is gradually eliminated with each iteration. Section 5 shows that the performance of the centralized algorithm can reach the Cramer Rao Lower Bound. The proposed distributed algorithm using a small number of bits can achieve the performance of the distributed gradient algorithm using unquantized data. HighlightsThe quantized distributed gradient method is applied for target localization.Particle swarm optimization is used to provide an initial estimate.A quantization error elimination scheme reduces communication cost.A quantization method for RSS measurements is presented.

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

[2]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[3]  Hing-Cheung So,et al.  Linear Least Squares Approach for Accurate Received Signal Strength Based Source Localization , 2011, IEEE Trans. Signal Process..

[4]  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.

[5]  Bernard Mulgrew,et al.  Distributed DOA estimation using clustering of sensor nodes and diffusion PSO algorithm , 2013, Swarm Evol. Comput..

[6]  Chin-Tau A. Lea,et al.  Received Signal Strength-Based Wireless Localization via Semidefinite Programming , 2009, GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference.

[7]  Pramod K. Varshney,et al.  Channel-Aware Tracking in Multi-Hop Wireless Sensor Networks with Quantized Measurements , 2013, IEEE Transactions on Aerospace and Electronic Systems.

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

[10]  Robert D. Nowak,et al.  Decentralized source localization and tracking [wireless sensor networks] , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Deborah Estrin,et al.  GPS-less low-cost outdoor localization for very small devices , 2000, IEEE Wirel. Commun..

[12]  Alfred O. Hero,et al.  Relative location estimation in wireless sensor networks , 2003, IEEE Trans. Signal Process..

[13]  Hao Guo,et al.  Real-Time Estimation of Sensor Node's Position Using Particle Swarm Optimization With Log-Barrier Constraint , 2011, IEEE Transactions on Instrumentation and Measurement.

[14]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[15]  Kegen Yu,et al.  Synchronization, TOA and position estimation for low-complexity LDR UWB devices , 2005, 2005 IEEE International Conference on Ultra-Wideband.

[16]  Alfred O. Hero,et al.  Energy-based sensor network source localization via projection onto convex sets , 2005, IEEE Transactions on Signal Processing.

[17]  Zheng Yang,et al.  High-Accuracy TDOA-Based Localization without Time Synchronization , 2013, IEEE Transactions on Parallel and Distributed Systems.

[18]  Ruggero Carli,et al.  Average consensus on networks with quantized communication , 2009 .

[19]  Richard M. Murray,et al.  Consensus problems in networks of agents with switching topology and time-delays , 2004, IEEE Transactions on Automatic Control.

[20]  Sinan Gezici,et al.  A Survey on Wireless Position Estimation , 2008, Wirel. Pers. Commun..

[21]  Parameswaran Ramanathan,et al.  Connectivity based location estimation scheme for wireless ad hoc networks , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[22]  Ian F. Akyildiz,et al.  Wireless sensor and actor networks: research challenges , 2004, Ad Hoc Networks.

[23]  John N. Tsitsiklis,et al.  Distributed subgradient methods and quantization effects , 2008, 2008 47th IEEE Conference on Decision and Control.

[24]  Alfred O. Hero,et al.  Using proximity and quantized RSS for sensor localization in wireless networks , 2003, WSNA '03.

[25]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[26]  René Vidal,et al.  Consensus with robustness to outliers via distributed optimization , 2010, 49th IEEE Conference on Decision and Control (CDC).

[27]  Bernie Mulgrew,et al.  Maximum Lilkelihood Source Localization in Wireless Sensor Network Using Particle Swarm Optimization , 2011 .

[28]  Q. Pan,et al.  Distributed, Robust Acoustic Source Localization in a Wireless Sensor Network , 2012, IEEE Transactions on Signal Processing.

[29]  Ioannis D. Schizas,et al.  Distributed LMS for Consensus-Based In-Network Adaptive Processing , 2009, IEEE Transactions on Signal Processing.

[30]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[31]  Santiago Zazo,et al.  A practical approach for outdoors distributed target localization in wireless sensor networks , 2012, EURASIP J. Adv. Signal Process..

[32]  Yonina C. Eldar,et al.  Convex Optimization in Signal Processing and Communications , 2009 .

[33]  Pramod K. Varshney,et al.  Channel Aware Target Localization With Quantized Data in Wireless Sensor Networks , 2009, IEEE Transactions on Signal Processing.

[34]  Stephen P. Boyd,et al.  Distributed Average Consensus with Time-Varying Metropolis Weights ? , 2006 .

[35]  P.K. Varshney,et al.  Target Location Estimation in Sensor Networks With Quantized Data , 2006, IEEE Transactions on Signal Processing.

[36]  P. Strevens.  Iii , 1985 .

[37]  Bernard Mulgrew,et al.  Distributed bearing estimation technique using diffusion particle swarm optimisation algorithm , 2012, IET Wirel. Sens. Syst..

[38]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[39]  Mikael Johansson,et al.  On Distributed Optimization Using Peer-to-Peer Communications in Wireless Sensor Networks , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.