Correlated Measurement Fusion Steady-state Kalman Filtering Algorithms and Their Optimality

Abstract

For the multisensor systems with correlated measurement noises and different measurement matrices,two correlated measurement fusion steady-state Kalman filtering algorithms are presented by using the weighted least squares (WLS)method.The principle is that a fused measurement equation is obtained by weighting the local measurement equations,and then it accompanies the state equation to realize the measurement fusion steady-state Kalman filtering. By using the information filter,it is proved that they are functionally equivalent to the centralized fusion steady-state Kalman filtering algorithm,so that they have the asymptotic global optimality,and they can reduce the computational burden.They can be applied to the measurement fusion filtering and deconvolution for multichannel autoregressive moving average(ARMA)signals.Two numerical simulation examples verify their functional equivalence.