In this paper, theoretical properties of a maximum-likelihood (ML) estimator of signal-to-noise ratio (SNR) is discussed. The three-paremter sine fit algorithm is employed on a finite and coherently sampled measurement set corrupted by additive white Gaussian noise. Under the Gaussian noise model, the least squares solution provided by the three-parameter sine fit is also ML estimator. Exact distribution and finite sample properties of the SNR estimate are derived. Moreover, an explicit expression for the mean squared error (MSE) of the estimator is given. Simulation results are shown to verify the underlying theoretical results.
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Royal Inst Technol, Signal Proc Lab, ACCESS Linnaeus Ctr, S-10044 Stockholm, SwedenRoyal Inst Technol, Signal Proc Lab, ACCESS Linnaeus Ctr, S-10044 Stockholm, Sweden
Negusse, Senay
Haendel, Peter
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Royal Inst Technol, Signal Proc Lab, ACCESS Linnaeus Ctr, S-10044 Stockholm, SwedenRoyal Inst Technol, Signal Proc Lab, ACCESS Linnaeus Ctr, S-10044 Stockholm, Sweden
Haendel, Peter
Zetterberg, Per
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Royal Inst Technol, Signal Proc Lab, ACCESS Linnaeus Ctr, S-10044 Stockholm, SwedenRoyal Inst Technol, Signal Proc Lab, ACCESS Linnaeus Ctr, S-10044 Stockholm, Sweden
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Royal Inst Technol, ACCESS Linnaeus Ctr, Signal Proc Lab, Stockholm, SwedenRoyal Inst Technol, ACCESS Linnaeus Ctr, Signal Proc Lab, Stockholm, Sweden