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Grid-less estimatio...
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Elvander, FilipLund University,Lunds universitet,Statistical Signal Processing Group,Forskargrupper vid Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
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Grid-less estimation of saturated signals
- Artikel/kapitelEngelska2017
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LIBRIS-ID:oai:lup.lub.lu.se:43044354-2e74-4f0a-9450-e95c56dc20f4
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https://lup.lub.lu.se/record/43044354-2e74-4f0a-9450-e95c56dc20f4URI
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https://doi.org/10.1109/ACSSC.2017.8335204DOI
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Språk:engelska
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Sammanfattning på:engelska
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This work proposes a frequency and amplitude estimator tailored for noise corrupted signals that have been clipped. Formulated as a sparse reconstruction problem, the proposed algorithm estimates the signal parameters by solving an atomic norm minimization problem. The estimator also exploits the waveform information provided by the clipped samples, incorporated in the form of linear constraints that have been augmented by slack variables as to provide robustness to noise. Numerical examples indicate that the algorithm offers preferable performance as compared to methods not exploiting the saturated samples.
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Swärd, JohanLund University,Lunds universitet,Statistical Signal Processing Group,Forskargrupper vid Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)mats-jsd
(författare)
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Jakobsson, AndreasLund University,Lunds universitet,Statistical Signal Processing Group,Forskargrupper vid Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Lund University Research Groups,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH(Swepub:lu)mats-aaj
(författare)
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Statistical Signal Processing GroupForskargrupper vid Lunds universitet
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Ingår i:2017 51st Asilomar Conference on Signals, Systems, and Computers, s. 372-3769781538618233
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