Abstract
This paper presents an approach how the first-order (gradient) parameter estimation algorithm can be enhanced to behave like a second-order algorithm; the key to achieve this is to apply "virtual whitening" to the data. This approach allows one to introduce more complex optimization criteria efficiently; as an example, it is demonstrated how an algorithm that is based on the kurtosis of the distribution can remarkably enhance the parameter estimation performance when unmodeled noise is present in the system.
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