Abstract
This paper demonstrates a method for performing spectral decomposition of a non-stationary signal with Heisenberg's minimum uncertainty wavelets. Due to Heisenberg's principle one cannot obtain arbitrarily narrow time and frequency resolution simultaneously. Therefore wavelets with optimum time and frequency localization would aid in best decomposition of a signal. This paper also introduces a new family of wavelets which was discovered during the research work. These wavelets have the optimum timefrequency localization and are termed as μ-pseudo Wavelets. The conventional decomposition approaches use Ricker or Morlet wavelet for analysis and its time frequency localizations are more than the minimum uncertainty wavelets. Due to the least uncertainty product these new wavelets open up a new dimension of application for various decomposition techniques. A synthetic example explains the result of the robustness of the minimum uncertainty wavelet decomposition.
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