Optimum filtering and control of randomly sampled systems
- 1 October 1967
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Automatic Control
- Vol. 12 (5) , 537-546
- https://doi.org/10.1109/tac.1967.1098673
Abstract
Kalman's concept of optimum filtering is interpreted in such a way that it can be applied to both nonlinear and linear systems with Gaussian or non-Gaussian statistics. The essential idea is to synthesize a generalized Kalman filter in two stages, a) propagation and b) measurement and correction. The analysis of each stage is independent of the other, and the generalized results are quite simple. In the present paper, the application of the above result is confined to linear systems: 1) Optimum filtering and interpolation of randomly sampled signals; for the interpolation problem it is assumed that the signals are measured exactly at the sampling instant. 2) Optimum filtering of related continuous and randomly sampled signals. 3) Optimum control of randomly sampled linear systems with quadratic cost criterion.Keywords
This publication has 8 references indexed in Scilit:
- Random sampling of random processes: Mean-square comparison of various interpolatorsIEEE Transactions on Automatic Control, 1966
- Sequential estimation when measurement function nonlinearity is comparable to measurement error.AIAA Journal, 1966
- Linear filtering for time-varying systems using measurements containing colored noiseIEEE Transactions on Automatic Control, 1965
- Multivariable Linear Filter Theory Applied to Space Vehicle GuidanceJournal of the Society for Industrial and Applied Mathematics Series A Control, 1964
- A General Solution for Linear, Sampled-Data ControlJournal of Basic Engineering, 1963
- New Results in Linear Filtering and Prediction TheoryJournal of Basic Engineering, 1961
- On linear control theoryTransactions of the American Institute of Electrical Engineers, Part II: Applications and Industry, 1961
- A New Approach to Linear Filtering and Prediction ProblemsJournal of Basic Engineering, 1960