Fast minimum variance resampling
T.F. Brennan, Paul Milenkovic · 2002
A novel method is introduced for resampling irregularly sampled data in the presence of noise. The estimator is minimum variance (MV) and minimum mean square error, under Gaussian assumptions, and well-conditioned in general. The Shannon-Whittaker sampling theorem is generalized to use raised cosine pulses as basis functions. It is shown that this generalization permits fast estimation with O(N) computational requirements for mildly oversampled signals (bandwidth less than 0.9 B/sub N/, where B/sub N/ is the Nyquist bandwidth of the resampled data). Also, some extensions of the inverse estimator and its error characteristics are discussed.