Kalman filters with Bayesian quadratic game fusion in networks

Distributed filtering in network is a fundamental problem in the field of network signal processing. Each node estimates or tracks some unknown state relying on the private observation and the fusion information from the network. Network fusion is generally a way of interaction over network, by which nodes can learn from each other and make decision mutually. Unlike conventional methods, we construct a distributed filter using Bayesian network game as a fusion tool, where all the nodes exchange their best strategies instead of exchanging local estimators. The proposed algorithm is a coalition of signal processing and game theory in network, which can be extended to more general signal processing and decision making models.

[1]  Soummya Kar,et al.  Consensus + innovations distributed inference over networks: cooperation and sensing in networked systems , 2013, IEEE Signal Processing Magazine.

[2]  Y. Zenou,et al.  Network Games with Incomplete Information , 2014 .

[3]  Zhu Lei,et al.  Multi-rate distributed fusion estimate of sensor networks based on descriptor system , 2015, The 27th Chinese Control and Decision Conference (2015 CCDC).

[4]  Ali H. Sayed,et al.  Diffusion LMS Strategies for Distributed Estimation , 2010, IEEE Transactions on Signal Processing.

[5]  Stergios I. Roumeliotis,et al.  Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals , 2008, IEEE Transactions on Signal Processing.

[6]  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.

[7]  F. Richard Yu,et al.  Energy-Efficient Resource Allocation for Heterogeneous Cognitive Radio Networks with Femtocells , 2012, IEEE Transactions on Wireless Communications.

[8]  Huijun Gao,et al.  Distributed Filtering for a Class of Time-Varying Systems Over Sensor Networks With Quantization Errors and Successive Packet Dropouts , 2012, IEEE Transactions on Signal Processing.

[9]  F. Richard Yu,et al.  Spectrum sharing and resource allocation for energy-efficient heterogeneous cognitive radio networks with femtocells , 2012, 2012 IEEE International Conference on Communications (ICC).

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

[11]  Lin Wang,et al.  Consensus based Distributed Unscented Information Filtering for Air Mobile Sensor Networks , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[12]  K. J. Ray Liu,et al.  Game theory for cognitive radio networks: An overview , 2010, Comput. Networks.

[13]  Kostas Berberidis,et al.  Distributed diffusion-based LMS for node-specific parameter estimation over adaptive networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Dimitri P. Bertsekas,et al.  Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey , 2015, ArXiv.

[15]  Amir Asif,et al.  Distributed Consensus $+$ Innovation Particle Filtering for Bearing/Range Tracking With Communication Constraints , 2015, IEEE Transactions on Signal Processing.

[16]  Victor M. Preciado,et al.  Analysis of equilibria and strategic interaction in complex networks , 2011, IEEE Conference on Decision and Control and European Control Conference.

[17]  Ali H. Sayed,et al.  Adaptive Networks , 2014, Proceedings of the IEEE.

[18]  Ruggero Carli,et al.  Distributed Kalman filtering based on consensus strategies , 2008, IEEE Journal on Selected Areas in Communications.

[19]  Hichem Snoussi,et al.  Distributed Variational Filtering for Simultaneous Sensor Localization and Target Tracking in Wireless Sensor Networks , 2012, IEEE Transactions on Vehicular Technology.

[20]  R. Olfati-Saber,et al.  Distributed Kalman Filter with Embedded Consensus Filters , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[21]  Alejandro Ribeiro,et al.  Bayesian Quadratic Network Game Filters , 2013, IEEE Transactions on Signal Processing.

[22]  Bo Hu,et al.  The incremental subgradient methods on distributed estimations in-network , 2013, Science China Information Sciences.

[23]  R. Kranton,et al.  Strategic Interaction and Networks , 2010 .

[24]  H. Vincent Poor,et al.  Social learning and bayesian games in multiagent signal processing: how do local and global decision makers interact? , 2013, IEEE Signal Processing Magazine.

[25]  Bo Hu,et al.  Recursive filters with Bayesian quadratic network game fusion , 2015, 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).