The minimum-variance filter and smoother are generalized to include Poisson-distributed measurement noise components. It is shown that the resulting filtered and smoothed estimates are unbiased. The use of the filter and smoother within expectation-maximization algorithms are described for joint estimation of the signal and Poisson noise intensity. Conditions for the monotonicity and asymptotic convergence of the Poisson intensity iterates are also established. An image restoration example is presented that demonstrates improved estimation performance at low signal-to-noise ratios.
Iterative filtering and smoothing of measurements possessing poisson noise
01.07.2015
369741 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Reentry filtering prediction and smoothing
AIAA | 1965
|British Library Online Contents | 2002
|Re-entry filtering, prediction, and smoothing
Engineering Index Backfile | 1966
|British Library Online Contents | 2017
|Re-entry filtering, prediction, and smoothing.
AIAA | 1966
|