Reduced dimension weighted measurement fusion Kalman filtering algorithm
会议名称：《2009中国控制与决策会议》
会议日期：2009年
学科分类：080902[工学-电路与系统] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
基 金：supported by the National Natural Science Foundationof China(No.60874063) the Innovation Scientific Research Foundation for Graduate Students of Heilongjiang Province(No.YJSCX2008-018HLJ) the Automatic Control Key Laboratory of Heilongjiang University
关 键 词：Weighted measurement fusion Linear unbiased minimum variance(LUMV) criterion Lagrange multiplier method and Reduced dimension algorithm
摘 要：正For the multisensor linear discrete time-invariant systems with correlated measurement noises and with dif ferent measurement matrices,based on the linear unbiased minimum variance criterion,a weighted measurement fusion Kalman filtering algorithm is *** is identical to that obtained by the Weighted Least Squares（WLS） method, and is numerically identical to the centralized fusion Kalman filtering algorithm,so that it has the global *** optimal weights are given by the Lagrange multiplier method,but its computation burden is *** order to reduce the computational burden,a reduced dimension weighted measurement fusion Kalman filtering algorithm is derived,which avoids the Lagrange multiplier method,and can significantly reduced the computational *** comparison of computational count between two algorithms is given.A simulation example shows effectiveness and correctness of the proposed algorithm.