A cooperative game-theoretic measurement allocation algorithm for localization in unattended ground sensor networks

This paper proposes a cooperative game-theoretic approach for efficient measurement allocation in unattended ground sensor networks when they are engaged in localizing a target. The game-theoretic approach evolves around the idea that localization is achieved as a result of collaboration among the nodes. Hence this process can be modeled as a cooperative game and the solution concept of the Shapley value can be exploited to determine the value of each node for localization. Furthermore, it is proved that by iteratively allocating measurements according to the Shapley value, stochastic observability-a measure of the predicted level of accuracy in localization-desirably improves.

[1]  Y. Oshman,et al.  Optimization of observer trajectories for bearings-only target localization , 1999 .

[2]  H. V. Trees.  Detection, Estimation, And Modulation Theory , 2001 .

[3]  L.M. Kaplan,et al.  Local node selection for localization in a distributed sensor network , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[4]  G. Pottie,et al.  Entropy-based sensor selection heuristic for target localization , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[5]  Ching Chun Li.  First Course in Population Genetics , 1976 .

[6]  R. Mohler,et al.  Nonlinear data observability and information , 1988 .

[7]  Feng Zhao,et al.  Collaborative In-Network Processing for Target Tracking , 2003, EURASIP J. Adv. Signal Process..

[8]  S. Hammel,et al.  Optimal observer motion for localization with bearing measurements , 1989 .

[9]  Sai-Ming Li,et al.  Forest fire monitoring with multiple small UAVs , 2005, Proceedings of the 2005, American Control Conference, 2005..

[10]  Tim Hesterberg,et al.  Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control , 2004, Technometrics.

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

[12]  J. F. C. Kingman,et al.  ON AN INEQUALITY IN PARTIAL AVERAGES , 1961 .

[13]  Ivan Kadar.  Optimum geometry selection for sensor fusion , 1998, Defense, Security, and Sensing.

[14]  Ivan Kadar,et al.  Self-organizing cooperative sensor network for remote surveillance: target tracking while optimizing the geometry between bearing-reporting sensors and the target , 2001, SPIE Defense + Commercial Sensing.

[15]  L. Shapley.  Cores of convex games , 1971 .

[16]  Robin J. Evans,et al.  An information theoretic approach to observer path design for bearings-only tracking , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[17]  L.M. Kaplan,et al.  Global node selection for localization in a distributed sensor network , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[18]  A. Gualtierotti.  H. L. Van Trees, Detection, Estimation, and Modulation Theory, , 1976 .

[19]  Feng Zhao,et al.  Information-driven dynamic sensor collaboration , 2002, IEEE Signal Process. Mag..

[20]  Dinesh Verma,et al.  A survey of sensor selection schemes in wireless sensor networks , 2007, SPIE Defense + Commercial Sensing.

[21]  James C. Spall,et al.  Introduction to Stochastic Search and Optimization. Estimation, Simulation, and Control (Spall, J.C. , 2007 .