Distributed uncorrelated optimal fusion algorithm and its application in estimation of paper basis weight
Xuebo Jin, Lin Yue-song · 2009
In practice, the state supervision of paper machine is generally obtained by the same kind of sensors, which can perform a more estimation result. For this special multisensor system, distributed uncorrelated optimal fusion algorithm is received by avoiding computing correlated estimation covariance based on the matrix operation. Compared with classical multisensor distributed-suboptimal algorithm and optimal fusion algorithm, this algorithm can adapt to the system with more than three sensors and has the advantages of the count capacity because it has no use for saving and computing the middle variable. Applications in estimation of paper basis weight show the developed algorithm has the excellent estimation performance.