A Distributed Algorithm for Managing Multi-target Identities in Wireless Ad-hoc Sensor Networks

This paper presents a scalable distributed algorithm for computing and maintaining multi-target identity information. The algorithm builds on a novel representational framework, Identity-Mass Flow, to overcome the problem of exponential computational complexity in managing multi-target identity explicitly. The algorithm uses local information to efficiently update the global multi-target identity information represented as a doubly stochastic matrix, and can be efficiently mapped to nodes in a wireless ad hoc sensor network. The paper describes a distributed implementation of the algorithm in sensor networks. Simulation results have validated the Identity-Mass Flow framework and demonstrated the feasibility of the algorithm.

[1]  Yaacov Ritov,et al.  Tracking Many Objects with Many Sensors , 1999, IJCAI.

[2]  Hai Tao,et al.  A Sampling Algorithm for Tracking Multiple Objects , 1999, Workshop on Vision Algorithms.

[3]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[4]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[5]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[6]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[7]  Feng Zhao,et al.  Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks , 2002, Int. J. High Perform. Comput. Appl..

[8]  Ian B. Rhodes,et al.  Decentralized sequential detection , 1989, IEEE Trans. Inf. Theory.

[9]  Satish Kumar,et al.  Next century challenges: scalable coordination in sensor networks , 1999, MobiCom.

[10]  Ingemar J. Cox,et al.  An Efficient Implementation of Reid's Multiple Hypothesis Tracking Algorithm and Its Evaluation for the Purpose of Visual Tracking , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Feng Zhao,et al.  Information-Driven Dynamic Sensor Collaboration for Tracking Applications , 2002 .

[12]  Robert R. Tenney,et al.  Detection with distributed sensors , 1980 .

[13]  I. Csiszár.  $I$-Divergence Geometry of Probability Distributions and Minimization Problems , 1975 .

[14]  I. Csiszár.  A geometric interpretation of Darroch and Ratcliff's generalized iterative scaling , 1989 .

[15]  Andrew Blake,et al.  A Probabilistic Exclusion Principle for Tracking Multiple Objects , 2004, International Journal of Computer Vision.

[16]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[17]  P. L. Combettes,et al.  Hilbertian convex feasibility problem: Convergence of projection methods , 1997 .

[18]  Leonidas J. Guibas,et al.  Kinetic data structures: a state of the art report , 1998 .