# Decentralized State Initialization with Delay Compensation for Multi-modal Sensor Networks

- 80 Downloads
- 2 Citations

## Abstract

Decentralized processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in smart sensor networks since they provide the ability to scale, reduce vulnerability, reduce communication, and share processing responsibilities among individual nodes. Sharing the processing responsibilities allows parallel processing of raw data at the individual nodes. However, this introduces other difficulties in multi-modal smart sensor networks, such as non-observability of the targets’ states at any individual node and various delays such as varying processing delays, communication delays and signal propagation delays for the different modalities. In this paper, we provide a novel algorithm to determine the initial probability distribution of multiple targets’ states in a decentralized manner. The targets’ state vectors consist of the targets’ positions and velocities on the 2D plane. Our approach can determine the state vector distribution even if the individual sensors alone are not capable of observing it. Our approach can also compensate for varying delays among the assorted modalities. The resulting distribution can be used to initialize various tracking algorithms. Our approach is based on Monte Carlo methods, where the state distributions are represented by a weighted set of discrete state realizations. A robust weighting strategy is formulated to account for missed detections, clutter and estimation delays. To demonstrate the effectiveness of the algorithm, we simulate a network with direction-of-arrival nodes and range-Doppler nodes.

### Keywords

data fusion decentralized processing initialization Monte Carlo methods multi-modal sensor networks## Notes

### Acknowledgement

Prepared through collaborative participation in the Advanced Sensors Consortium sponsored by the U. S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-02-0008.

### References

- 1.J. Manyika and H. Durrant-Whyte,
*Data Fusion and Sensor Management: A Decentralized Information—Theoretic Approach*, Prentice Hall, 1994.Google Scholar - 2.G. J. Pottie and W. J. Kaiser, “Wireless integrated network sensors,”
*Commun. ACM*, vol. 43, 2000, pp. 51–58 (May).CrossRefGoogle Scholar - 3.Y. Wong, J. Wu, L. Ngoh, and W. Wong, “Collaborative data fusion tracking in sensor networks using monte carlo methods,” in
*Proceedings. 29th Annual IEEE International Conference on Local Computer Networks*, 2004.Google Scholar - 4.M. Liggins II, C. Chong, I. Kadar, M. Alford, V. Vannicola, and S. Thomopoulos, “Distributed fusion architectures and algorithms for target tracking,” in
*Proc. IEEE*, 1997.Google Scholar - 5.P. Storms, J van Veelen, and E. Boasson, “A process distribution approach for multisensor data fusion systems based on geographical dataspace partitioning,”
*IEEE Trans. Parallel Distrib. Syst.*, vol. 16, 2005, pp. 14–23(Jan.).CrossRefGoogle Scholar - 6.S. Balasubramanian, I. Elangovan, S. Jayaweera, and K. Namuduri, “Distributed and collaborative tracking for energy-constrained ad-hoc wireless sensor networks,”
*IEEE Wireless Communications and Networking Conference*, vol. 3, 2004, pp. 1732–1737.Google Scholar - 7.J. Liu, M. Chu, J. Liu, J. Reich, and F. Zhao, “Distributed state representation for tracking problems in sensor networks,”
*Third International Symposium on Information Processing in Sensor Networks*, 2004, pp. 234–242.Google Scholar - 8.I. Leichter, M. Lindenbaum, and E. Rivlin, “A probabilistic framework for combining tracking algorithms,” in
*CVPR 2004*, WDC, June 27–July 2 2004.Google Scholar - 9.V. Cevher and J. H. McClellan, “General direction-of-arrival tracking with acoustic nodes,”
*IEEE Trans. Signal Process.*, vol. 53, 2005, pp. 1–12(Jan.).CrossRefGoogle Scholar - 10.M. Orton and W. Fitzgerald, “A Bayesian approach to tracking multiple targets using sensor arrays and particle filters,”
*IEEE Trans. Signal Process.*, vol. 50, no. 2, 2002, pp. 216–223(February).CrossRefGoogle Scholar - 11.R. R. Allen and S. S. Blackman, “Implementation of an angle-only tracking filter,”
*SPIE Proc.*, vol. 1481, 1991, pp. 292–303.CrossRefGoogle Scholar - 12.A. Farina, “Target tracking with bearings-only measurements,”
*Elsevier Signal Processing*, vol. 78, 1999, pp. 61–78.MATHCrossRefGoogle Scholar - 13.Y. Zhou, P.C. Yip, and H. Leung, “Tracking the direction-of-arrival of multiple moving targets by passive arrays: algorithm,”
*IEEE Trans. Signal Process.*, vol. 47, no. 10, 1999, pp. 2655–2666 (October).CrossRefGoogle Scholar - 14.J. Sanchez-Araujo and S. Marcos, “An efficient PASTd-algorithm implementation for multiple direction of arrival tracking,”
*IEEE Trans. Signal Process.*, vol. 47, 1999, pp. 2321–2324 (August).CrossRefGoogle Scholar - 15.V. J. Aidala, “Kalman filter behavior in bearings-only tracking applications,”
*IEEE Trans. Aerosp. Electron. Syst.*, vol. AES-15, 1979, pp. 29–39 (January).CrossRefGoogle Scholar - 16.S. Hong, R. Evans, and H. Shin, “Optimization of waveform and detection threshold for range and range-rate tracking in clutter,”
*IEEE Trans. Aerosp. Electron. Syst.*, vol. 41, 2005, pp. 17–33.CrossRefGoogle Scholar - 17.E. Hughes and M. Lewis, “Intelligent agents for radar systems,”
*Electronics Systems and Software*, vol. 3, Feb.–March 2005, pp. 39–43.CrossRefGoogle Scholar - 18.A. Doucet, “On sequential simulation-based methods for Bayesian filtering,” Tech. Rep. CUED/F-INFENG/TR.310, Department of Engineering, University of Cambridge, 2001.Google Scholar
- 19.J. R. Munkres,
*Analysis on Manifolds*, Perseus Books, 1990.Google Scholar - 20.Y. Bar-Shalom and T. Fortmann,
*Tracking and Data Association*, Academic, 1988.Google Scholar - 21.M. Isard and A. Blake, “Condensation—conditional density propagation for visual tracking,”
*Int. J. Comput. Vis.*, vol. 29, 1998, pp. 5–28.CrossRefGoogle Scholar - 22.M. J. Coates, “Distributed particle filtering for sensor networks,”
*International Symposium on Information Processing in Sensor Networks*, 2004.Google Scholar