Open Science Research Excellence
@article{(International Science Index):,
  title    = {UD Covariance Factorization for Unscented Kalman Filter using Sequential Measurements Update},
  author    = {H. Ghanbarpour Asl and  S. H. Pourtakdoust},
  country   = {},
  abstract  = {Extended Kalman Filter (EKF) is probably the most
widely used estimation algorithm for nonlinear systems. However,
not only it has difficulties arising from linearization but also many
times it becomes numerically unstable because of computer round off
errors that occur in the process of its implementation. To overcome
linearization limitations, the unscented transformation (UT) was
developed as a method to propagate mean and covariance
information through nonlinear transformations. Kalman filter that
uses UT for calculation of the first two statistical moments is called
Unscented Kalman Filter (UKF). Square-root form of UKF (SRUKF)
developed by Rudolph van der Merwe and Eric Wan to
achieve numerical stability and guarantee positive semi-definiteness
of the Kalman filter covariances. This paper develops another
implementation of SR-UKF for sequential update measurement
equation, and also derives a new UD covariance factorization filter
for the implementation of UKF. This filter is equivalent to UKF but
is computationally more efficient.},
    journal   = {International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering},  volume    = {1},
  number    = {10},
  year      = {2007},
  pages     = {564 - 572},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 10, 2007},