A state-space formulation of the causal linear prefiltering problem in sampled-data systems

In this paper, we consider the problem of linearly prefiltering and coding a random process which is to be sampled in time and transmitted over a noisy channel. We take the linear reconstruction filter at the receiving end to be fixed and known, and we then determine the form of the optimum linear p...

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Bibliographic Details
Published in:Information sciences 1978, Vol.14 (1), p.75-87
Main Authors: Yeh, Yern, Rushforth, Craig K., Tal, Jacob
Format: Article
Language:English
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Summary:In this paper, we consider the problem of linearly prefiltering and coding a random process which is to be sampled in time and transmitted over a noisy channel. We take the linear reconstruction filter at the receiving end to be fixed and known, and we then determine the form of the optimum linear prefilter subject to a power constraint on its output. If the process to be filtered is described using a state-space representation, the prefilter can be realized as a continuous Kalman filter followed by a digital filter which in general has memory. If the duration of the impulse response of the reconstruction filter is less than the interval between adjacent samples, the digital filter becomes a memoryless matrix coder.
ISSN:0020-0255
1872-6291
DOI:10.1016/0020-0255(78)90028-2