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New Multisensor Data Fusion Method Based on Probabilistic Grids Representation

Authors: Zhichao Zhao, Yi Liu, Shunping Xiao


A new data fusion method called joint probability density matrix (JPDM) is proposed, which can associate and fuse measurements from spatially distributed heterogeneous sensors to identify the real target in a surveillance region. Using the probabilistic grids representation, we numerically combine the uncertainty regions of all the measurements in a general framework. The NP-hard multisensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion method, the JPDM method dose not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRLB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.

Keywords: Cramer-Rao lower bound (CRLB), data fusion, probabilistic grids, joint probability density matrix, localization, sensor network.

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[1] M. E. Liggins, D. L. Hall, J. Llinas, Handbook of Multisensor Data Fusion: Theory and Practice. Boca Raton, FL: CRC Press, 2009.
[2] H. B. Mitchell, Multi-Sensor Data Fusion. Berlin: Springer, 2007.
[3] X. Sheng, Y. Hu, "Maximum likelihood multiple-source location using acoustic energy measurements with wireless sensor network," IEEE Trans. Signal Process., vol. 53, no 1, pp. 44-53, Jan. 2005.
[4] D. Somnath, Y. Murali, P. Krishna, and Y. Bar-Shalom, "A generalized S-D assignment algorithm for multisensor-multitarget state estimation," IEEE Trans. Aerosp. Electron. Syst., vol. 33, no. 2, pp. 523-538, Apr. 1997.
[5] T. Kirubarajan, H. Wang, Y. Bar-Shalom, and K. R. Pattipati, "m-Best S-D assignment algorithm with application to multitarget tracking," IEEE Trans. Aerosp. Electron. Syst., vol. 37, no. 1, pp. 22-39, Jan. 2001.
[6] S. Deb, K. R. Pattipati, and Y. Bar-Shalom, "A multisensor-multitarget data association algorithm for heterogeneous sensors," IEEE Trans. Aerosp. Electron. Syst., vol. 29, no. 2, pp. 560-568, Apr. 1993.
[7] D. Musicki, "Multi-Target Tracking using Multiple Passive Bearings-Only Asynchronous Sensors," IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 3, pp. 1151-1160, July 2008.
[8] A. Elfes, "Sonar-based real-world mapping and navigation," IEEE Trans. Robot. Autom., vol. 3, no. 3, pp. 249-265, June 1987.
[9] H. P. Moravec, and A. Elfes, "High resolution maps from wide angle sonar," in Proc. IEEE Conf. Robotics and Automation, pp. 116-121, 1985.
[10] A. Birk, S. Carpin, "Merging occupancy grid maps from multiple robots," Proc. IEEE, vol. 94, no. 7, pp. 1384-1397, July 2006.