Fusion Techniques for Reliable Information: A Survey <s> Distributed System (DS) <s> Under the assumption of independent observation noises across sensors, Bar-Shalom and Campo proposed a distributed fusion formula for two-sensor systems, whose main calculation is the inverse of submatrices of the error covariance of two local estimates instead of the inverse of the error covariance itself. However, the corresponding simple estimation fusion formula is absent in a general distributed multisensor system. In this paper, an efficient iterative algorithm for distributed multisensor estimation fusion without any restrictive assumption on the noise covariance (i.e., the assumption of independent observation noises across sensors and the two-sensor system, and the direct computation of the Moore-Penrose generalized inverse of the joint error covariance of local estimates are not necessary) is presented. At each iteration, only the inverse or generalized inverse of a matrix having the same dimension as the error covariance of a single-sensor estimate is required. In fact, the proposed algorithm is a generalization of Bar-Shalom and Campo's fusion formula and reduces the computational complexity significantly since the number of iterative steps is less than the number of sensors. An example of a three-sensor system shows how to implement the specific iterative steps and reduce the computational complexities <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Distributed System (DS) <s> This paper develops a data fusion algorithm of multisensor system based on sequential filtering in distributed network. In actual multisensor dynamic system, in every time- step, observations obtained from local sensors are transformed to the center coordinate system. And then, they are transmitted to the center processor in turn. In the center processor, observations are used to update sequentially the previous global estimate with Kalman filters. Thus one can obtain a global estimate of the state at this step based on the global information. In this paper, the deductive process of the algorithm is presented. On the condition of the same accuracy , we compare the computational cost of new algorithm with that of the centralized data fusion algorithm to show the superiority of the former.Finally the simulation indicates the validity of the algorithm. <s> BIB002
Multiple sensors, platforms, or information sources, which are fused to generate information over networks, have led to develop DS. The key problem for fusion in DS is to determine the coefficient of the data  . In BIB002 , sequential filtering and consensus algorithms are provided for scalable sensor fusions with dynamic DS networks. In BIB001 , efficient algorithms for an optimal linear estimation fusion are used with DS. Underlying relationships among objects are assumed to unknown to the automated system or human.