Abstract
When doing simulations one of the worst problems that can occur is an unstable system model. These unstable models often appear in systems identification problems where the system has a non-minimum phase model. This situation leads to an unstable inverse system. The instability means that the system model must be redefined, even though the non-minimum phase model is most accurate. Or a method for deconvolution will be chosen that assumes the system is minimum phase, even though it is not. Often these inaccurate fixes or assumptions are not necessary. Cases where short strings of data are being processed can use the unstable inverse and produce good results. By using the superposition feature of linear systems it is possible to increase the useful run time of these unstable deconvolvers.
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