Title :
Predictive transform for optimum digital signal processing
Author_Institution :
Coll. of Staten Island, City Univ. of New York, NY, USA
Abstract :
A unified approach to optimum digital signal modeling, coding, estimation, and control is outlined. The origins of the proposed technique reside in a minimum-mean-square-error predictive transform (PT) coding formulation which yields a decoder that is a whitening PT model of the source whose output is to be encoded. This PT decoder is integrated with Kalman estimation (prediction, filtering and smoothing) as well as linear quadratic Gaussian control to produce a comprehensive approach to estimation and control that directly addresses the signal modeling problem. An additional bonus of the proposed unified approach to optimum digital signal processing is that a transformation mechanism arises which leads to significant design and implementation simplifications when the dimension of the signal is large. These ideas are illustrated with a 2D monochrome image application, where it is found that the design and implementation requirements of a PT smoother are often much less than those of a Kalman smoother with strictly similar performance. Specifically, the design computational burden is often improved by a factor greater than four and the implementation complexity burden by a factor close to four
Keywords :
Kalman filters; computational complexity; computerised signal processing; encoding; filtering and prediction theory; least squares approximations; optimisation; transforms; 2D monochrome image; Kalman estimation; Kalman smoother; computational complexity; decoder; design computational burden; encoding; filtering; implementation complexity; linear quadratic Gaussian control; minimum-mean-square-error predictive transform coding formulation; optimum digital signal processing; signal coding; signal control; signal estimation; signal modeling; whitening model; Decoding; Digital signal processing; Kalman filters; Mean square error methods; Nonlinear filters; Predictive models; Signal processing; State estimation; State-space methods; Transform coding;
Conference_Titel :
Systems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on
Conference_Location :
Cambridge, MA
DOI :
10.1109/ICSMC.1989.71500