Self-tuning filters and predictors for two-dimensional systems Part 1: Algorithms
- 1 August 1985
- journal article
- research article
- Published by Taylor & Francis in International Journal of Control
- Vol. 42 (2) , 457-478
- https://doi.org/10.1080/00207178508933375
Abstract
The filtering of two-dimensional (2-D) signals is treated using a self-tuning technique based on a truncated innovations model of the data. The resultant algorithms offer two key advantages over their fixed-coefficient counterparts. First, the self-tuning filters quickly and automatically set their own coefficients, thus avoiding the normal off-line design cycle. Secondly, self-tuning filters can function in an adaptive manner, such that the filter retunes to track time variations in the two-dimensional data. The self-tuning algorithms are formulated in terms of input/output models and thus complement the more usual state-space approach to the 2-D filtering problem.Keywords
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