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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: Sensor Network, Localization, Data fusion, Cramer-Rao lower bound (CRLB), probabilistic grids, joint probability density matrix

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