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We address an estimation problem of nonlinear dynamic system through a large-scale sensor network. Even though much research has been done in data fusion, the extension to nonlinear dynamic system is recently focused. The main difficulty in data fusion of nonlinear dynamic system comes from that effective nonlinear filters do not allow the information form. In this paper, two algorithms are considered to implement distributed Kalman filtering for a large-scale sensor network. Data fusion problem for a largescale sensor network is tackled by using Kalman-Consensus filter (KCF) whose scalability is suitable for a large-scale sensor network with random topology. Based on KCF fusion algorithm, Sigma-Point Information filter (SPIF) is proposed as a micro-filter of KCF to handle the nonlinear dynamic system. Because of its information fusion structure, it is simple and intuitive to be combined with the consensus algorithm. Newly proposed algorithm called Consensus Sigma-Point Information Filter (CSPIF) shows us the improved accuracy compared with local estimates.