By Yanwei Wang

Spectral estimation is critical in lots of fields together with astronomy, meteorology, seismology, communications, economics, speech research, clinical imaging, radar, sonar, and underwater acoustics. such a lot current spectral estimation algorithms are devised for uniformly sampled complete-data sequences. notwithstanding, the spectral estimation for facts sequences with lacking samples can also be very important in lots of purposes 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 how you can expand the present spectral estimation suggestions to accommodate those missing-data samples. lately, nonparametric adaptive filtering dependent thoughts were constructed effectively for varied missing-data difficulties. jointly, those algorithms offer a finished toolset for the missing-data challenge dependent solely at the nonparametric adaptive filter-bank methods, that are strong and actual, and will offer excessive answer and occasional sidelobes. during this publication, we current those algorithms for either one-dimensional and two-dimensional spectral estimation difficulties.

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