Multisensor Weighted Measurement Fusion Kalman Filter with Correlated Noises System

For the multisensor stochastic control systems with different measurement matrices and correlated noises, a new weighted measurement fusion (WMF) estimation algorithm is presented by using full-rank decomposition of matrix and weighted least squares theory. The newly presented algorithm can handle the fused filtering, smoothing, and prediction problems for the state in a unified framework and prove the global optimality, which indicates that the estimating result is equivalent to centralized fusion (CF) Kalman estimating result. However, it can obviously reduce the computational burden compared with CF, so it is convenient for application in real time. A simulation result will show the effectiveness of the proposed algorithm. Copyright © 2014 IFSA Publishing, S. L.