dc.contributor.author | Pourafzal, Alireza | |
dc.contributor.author | Škrabánek, Pavel | |
dc.contributor.author | Cheffena, Michael | |
dc.contributor.author | Yildirim, Sule | |
dc.contributor.author | Roi-Taravella, Thomas | |
dc.date.accessioned | 2024-03-14T07:02:11Z | |
dc.date.available | 2024-03-14T07:02:11Z | |
dc.date.created | 2024-01-15T11:04:31Z | |
dc.date.issued | 2024 | |
dc.identifier.issn | 1051-2004 | |
dc.identifier.uri | https://hdl.handle.net/11250/3122265 | |
dc.description.abstract | We propose a single-tone frequency estimator of a one-dimensional complex signal in complex white Gaussian noise. The estimator is based on the subspace approach and the unitary transformation. Due to its low space and time-complexity, we name the estimator as Low complexity Unitary Principal-singular-vector Utilization for Model Analysis (LUPUMA). Regardless of the observation length, LUPUMA provides a uniform estimation variance over the whole frequency range, while achieving the lowest time-complexity among subspace methods. The proposed estimator asymptotically reaches the Cramér-Rao Lower Bound. For short observations, the signal-to-noise ratio threshold of LUPUMA corresponds to the threshold of the maximum likelihood estimator. The low space and time-complexity along with the stable and state-of-the-art estimation performance for short observations make LUPUMA an ideal candidate for applications with a limited number of signal samples, limited computational power, limited memory, and for applications that require rapid processing time (low latency). | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.title | Low complexity subspace approach for unbiased frequency estimation of a complex single-tone | en_US |
dc.title.alternative | Low complexity subspace approach for unbiased frequency estimation of a complex single-tone | en_US |
dc.type | Journal article | en_US |
dc.description.version | submittedVersion | en_US |
dc.source.volume | 145 | en_US |
dc.source.journal | Digital signal processing (Print) | en_US |
dc.source.issue | 104304 | en_US |
dc.identifier.doi | 10.1016/j.dsp.2023.104304 | |
dc.identifier.cristin | 2226432 | |
cristin.ispublished | true | |
cristin.fulltext | preprint | |
cristin.qualitycode | 1 | |