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- W2539165712 abstract "Estimating complex spectra is a widespread operation in signal processing and in some applications a high dynamic range, which requires low sidelobe levels, is essential. For data with uniform sample spacing, weightings are commonly applied to Fourier transforms to suppress sidelobes, increasing dynamic range at the cost of some loss of spectral resolution. However, if a significant proportion of the samples lack data, conventional weightings suffer from high sidelobe levels. While a wide range of linear and non-linear techniques has been proposed to tackle this problem, they are not suitable in applications, such as Synthetic Aperture Radar (SAR), which require a high dynamic range. We propose criteria that allow optimal weights to be computed for any pattern of sample times. The resulting weighted Fourier transform has advantages of inexpensive computation, easily understood characteristics arising from its linearity, a position-independent impulse response and importantly the transform is phase preserving. Potential applications include wideband radar, where spectral gaps are needed for coexistence with other systems; multifunction radar, where imaging is interrupted by other tasks; and bistatic radar, where the spectrum of a transmitter of opportunity may be incomplete. In the context of SAR, high dynamic range, phase preserving spectral estimation supports post-processing such as interferometry and coherent change detection." @default.
- W2539165712 created "2016-10-28" @default.
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- W2539165712 date "2016-09-01" @default.
- W2539165712 modified "2023-09-26" @default.
- W2539165712 title "High Dynamic Range Spectral Estimation for Incomplete Time Series" @default.
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- W2539165712 doi "https://doi.org/10.1109/sspd.2016.7590603" @default.
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