A SEPARATE BIAS U-D FACTORIZATION FILTER

Two forms of Friedland's separate bias estimation algorithm with U-D f actorization of the covariance matrices are provided. Each is suited t o implementation in a particular computing environment. (We consider M ATLAB and compiled computer languages.) We reduce the computation time substantially, primarily at the time propagation stage, by using a se parated bias formulation, while retaining the desirable numerical prop erties of the U-D factorization. The perecentage reduction typically i ncreases with ratio of bias state dimension to dynamic state dimension . A numerical evaluation is given for the MATLAB algorithm.