Square root filtering in time-sequential estimation of random fields

Toshio M. Chint, W. Clem Karl, Arthur J. Mariano, Alan S. Willsky

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


As a time-sequential and Bayesian front-end for image sequence processing, we consider the square root information (SRI) realization of Kalman filter. The computational complexity of the filter due to the dimension of the problem - the size of the state vector is on the order of the number of pixels in the image frame - is decreased drastically using a reduced-order approximation exploiting the natural spatial locality in the random field specifications. The actual computation for the reduced-order SRI filter is performed by an iterative and distributed algorithm for the unitary transformation steps, providing a potentially faster alternative to the common QR factorization-based methods. For the space-time estimation problems, near-optimal solutions can be obtained in a small number of iterations (e.g. less than 10), and each iteration can be performed in a finely parallel manner over the image frame, an attractive feature for a dedicated hardware implementation.

Original languageEnglish (US)
Pages (from-to)51-58
Number of pages8
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Apr 8 1993
EventImage and Video Processing 1993 - San Jose, United States
Duration: Jan 31 1993Feb 5 1993

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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