Title :
On Kalman filter solution of space-time interpolation
Author_Institution :
Jet Propulsion Lab., California Inst. of Technol., Pasadena, CA, USA
fDate :
4/1/2001 12:00:00 AM
Abstract :
The approximate Kalman filtering algorithm presented previously (see ibid., vol.3, p.773-88, Nov. 1994) for image sequence processing can introduce unacceptable negative eigenvalues in the information matrix and can have degraded performance in some applications. The improved algorithm presented in this note guarantees a positive definite information matrix, leading to more stable filter performance
Keywords :
Kalman filters; eigenvalues and eigenfunctions; filtering theory; image sequences; interpolation; matrix algebra; Kalman filter solution; approximate Kalman filtering algorithm; image sequence processing; negative eigenvalues; positive definite information matrix; space-time interpolation; stable filter performance; Covariance matrix; Eigenvalues and eigenfunctions; Geophysics computing; Image reconstruction; Image sequences; Interpolation; Kalman filters; Markov random fields; Satellites; Sparse matrices;
Journal_Title :
Image Processing, IEEE Transactions on
