Multisensor Data Fusion : From Algorithm and Architecture Design to Applications

In this chapter, we focus on a cognitive wireless sensor network (WSN), where a primary WSN (PWSN) is co-located with a cognitive (or secondary) WSN (CWSN). The shared frequency spectrum can be “freely” assigned by the PWSN. In particular, this spectrum is divided into disjoint “subchannels” and each subchannel is assigned (in a unique way) to a node of the PWSN. We assume that the nodes of the CWSN cooperate to sense the frequency spectrum and estimate the free subchannels which can be used to transmit their data. The key contribution of this work is two-fold. First, the sensing correlation among the secondary nodes is exploited to improve the reliability of the decision, taken by a secondary fusion center (FC), on the occupation status (by a node of the PWSN) of each subchannel. In this context, we provide a simple model for characterizing the local sensing performance per subchannel, in terms of probabilities of missed detection (MD) and false alarm (FA). Then, we analyze the use of joint source channel coding (JSCC) schemes in the CWSN, in order to further exploit the source correlation to make the final decision of the secondary FC on the status of the frequency subchannels more reliable.

[1]  Joseph Mitola,et al.  Cognitive radio: making software radios more personal , 1999, IEEE Wirel. Commun..

[2]  Anna Freud,et al.  Design And Analysis Of Modern Tracking Systems , 2016 .

[3]  S.S. Blackman,et al.  Multiple hypothesis tracking for multiple target tracking , 2004, IEEE Aerospace and Electronic Systems Magazine.

[4]  Eric Lefevre,et al.  Multi-sensor multi-target tracking with robust kinematic data based credal classification , 2013, 2013 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF).

[5]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[6]  R. Singer.  Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets , 1970, IEEE Transactions on Aerospace and Electronic Systems.

[7]  Sameer Singh,et al.  Approaches to Multisensor Data Fusion in Target Tracking: A Survey , 2006, IEEE Transactions on Knowledge and Data Engineering.

[8]  Daniel Jolly,et al.  Object association with belief functions, an application with vehicles , 2011, Inf. Sci..

[9]  Véronique Berge-Cherfaoui,et al.  Optimal Object Association in the Dempster–Shafer Framework , 2014, IEEE Transactions on Cybernetics.

[10]  Geoffrey Ye Li,et al.  Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks , 2007, IEEE Transactions on Wireless Communications.

[11]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[12]  W. Dale Blair,et al.  IMM algorithm for tracking targets that maneuver through coordinated turns , 1992, Defense, Security, and Sensing.

[13]  Gianluigi Ferrari,et al.  Feedback Power Control Strategies in Wireless Sensor Networks with Joint Channel Decoding , 2009, Sensors.

[14]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[15]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[16]  Sailes K. Sengijpta.  Fundamentals of Statistical Signal Processing: Estimation Theory , 1995 .

[17]  Philippe Smets,et al.  Decision making in the TBM: the necessity of the pignistic transformation , 2005, Int. J. Approx. Reason..

[18]  Branko Ristic,et al.  Kalman filter and joint tracking and classification based on belief functions in the TBM framework , 2007, Inf. Fusion.

[19]  Dmitri Moltchanov,et al.  Distance distributions in random networks , 2012, Ad Hoc Networks.

[20]  Simon Haykin,et al.  Fundamental Issues in Cognitive Radio , 2007 .

[21]  Philippe Smets,et al.  Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem , 1993, Int. J. Approx. Reason..

[22]  Anant Sahai,et al.  What is a Spectrum Hole and What Does it Take to Recognize One? , 2009, Proceedings of the IEEE.

[23]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[24]  Alexander M. Wyglinski,et al.  A Spectrum Surveying Framework for Dynamic Spectrum Access Networks , 2009, IEEE Transactions on Vehicular Technology.

[25]  Geoffrey Ye Li,et al.  Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks , 2007, IEEE Transactions on Wireless Communications.

[26]  Isabelle Bloch.  Information combination operators for data fusion: a comparative review with classification , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[27]  Arthur P. Dempster,et al.  A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[28]  Branko Ristic,et al.  On target classification using kinematic data , 2004, Inf. Fusion.

[29]  Philippe Smets,et al.  The Transferable Belief Model , 1991, Artif. Intell..

[30]  Philippe Smets,et al.  Belief functions on real numbers , 2005, Int. J. Approx. Reason..

[31]  Gianluigi Ferrari,et al.  Information fusion in wireless sensor networks with source correlation , 2014, Inf. Fusion.

[32]  Pramod K. Varshney,et al.  Distributed detection in a large wireless sensor network , 2006, Inf. Fusion.

[33]  François Delmotte,et al.  A New Parameterless Credal Method to Track-to-Track Assignment Problem , 2014, Belief Functions.

[34]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[35]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[36]  François Bourgeois,et al.  An extension of the Munkres algorithm for the assignment problem to rectangular matrices , 1971, CACM.

[37]  Philippe Smets,et al.  Target identification based on the transferable belief model interpretation of dempster-shafer model , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[38]  Eric Lefevre,et al.  Object tracking and credal classification with kinematic data in a multi-target context , 2014, Inf. Fusion.