By Yanwei Wang

Spectral estimation is critical in lots of fields together with astronomy, meteorology, seismology, communications, economics, speech research, scientific imaging, radar, sonar, and underwater acoustics. so much present spectral estimation algorithms are devised for uniformly sampled complete-data sequences. even though, the spectral estimation for information sequences with lacking samples is additionally vital in lots of functions starting from astronomical time sequence research to artificial aperture radar imaging with angular variety. For spectral estimation within the missing-data case, the problem is the right way to expand the present spectral estimation innovations to accommodate those missing-data samples. lately, nonparametric adaptive filtering established concepts were built effectively for varied missing-data difficulties. jointly, those algorithms offer a finished toolset for the missing-data challenge dependent completely at the nonparametric adaptive filter-bank methods, that are strong and actual, and will offer excessive solution and occasional sidelobes. during this booklet, we current those algorithms for either one-dimensional and two-dimensional spectral estimation difficulties.

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