Abstract
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.
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I. Csiszar, “A geometric interpretation of Darroch and Ratcliff’s generalized iterative scaling”, Annals of Probability, 3, 146–158, 1975.
Reid, D.B., “An Algorithm for Tracking Multiple Targets”, IEEE Trans. on Automatic Control, No. 6, December 1979, pp. 843–854
R. R. Tenny and N. R. Sandell Jr., “Detection with Distributed Sensors”, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-17, No. 4, July 1981
Y. Bar-Shalom and T. E. Fortmann, Tracking and Data Association, Academic Press, New York, 1988
H. R. Hashemi and I. B. Rhodes, “Decentralized Sequential Detection”, IEEE Transactions on Information Theory, Vol. 35, No. 3, May 1989
T. M. Cover and J. A. Thomas, “Elements of Information Theory”, Wiley, 1991
I. J. Cox and S. L. Hingorani, “An Efficient Implementation of Reid’s Multiple Hypohtesis Tracking Algorithm and its Evaluation for the Purpose of Visual Tracking,” IEEE Trans. on PAMI, Vol 18., No. 2, pp. 138–150, Feb. 1996
P. L. Combettes, “Hilbertian Convex Feasibility Problem: Convergence of Projection Methods” Appl. Math. Optim. 35:311–330, 1997
M. Isard and A. Blake, “CONDENSATION — Conditional Density Propagation for Visual Tracking”, Int. J. Computer Vision, 1998
L. J. Guibas, “Kinetic data structures — a state of the art report,” Proc. Workshop Algorithmic Found. Robot., pages 191–209, A. K. Peters, Wellesley, MA, 1998
H. Tao, H.S. Sawhney and R. Kumar, “A Sampling Algorithm for Tracking Multiple Objects,” Proc. of the IEEE Wkshp. on Vision Algorithms, Corfu, Greece, Sep. 1999
H. Pasula, S. Russel, M. Ostland and Y. Ritov, “Tracking Many Objects with many sensors,” Int. Joint Conf. on Artificial Intelligence (IJCAI), Stockholm, pages 1160–1171, 1999.
D. Estrin, R. Govindan, J. Heidemann, and S. Kumar, “Next century challenges: Scalable coordination in sensor networks”, Proceedings of the Fifth Annual International Conference on Mobile Computing and Networks, Seattle, Washington, August 1999
P. Gupta and P.R. Kumar, “The Capacity of Wireless Networks”, IEEE Trans. Inform. Theory, 46(2):388–404, 2000.
J. MacCormick and A. Blake, “Probabilistic exclusion and partitioned sampling for multiple object tracking.”, Intl J. Computer Vision, 39(1):57–71, 2000.
M. Chu, H. Haussecker, and F. Zhao, “Scalable Information-Driven Sensor Query and Routing for ad hoc Heterogeneous Sensor Network”, International Journal of High Performance Computing Applications, 2002
F. Zhao, J. Shin, and J. Reich, “Information Driven Dynamic Sensor Collaboration for Tracking Application”, IEEE Signal Processing Magazine 2002 March
Wei Ye, John Heidemann and Deborah Estrin, “An Energy-Efficient MAC Protocol for Wireless Sensor Networks”, IEEE INFOCOM 2002, June, 2002.
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Shin, J., Guibas, L.J., Zhao, F. (2003). A Distributed Algorithm for Managing Multi-target Identities in Wireless Ad-hoc Sensor Networks. In: Zhao, F., Guibas, L. (eds) Information Processing in Sensor Networks. IPSN 2003. Lecture Notes in Computer Science, vol 2634. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36978-3_15
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DOI: https://doi.org/10.1007/3-540-36978-3_15
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