List of references on spectral line analysis
Signal Processing
Subspace-based frequency estimation of sinusoidal signals in alpha-stable noise
Signal Processing - Signal processing with heavy-tailed models
On some detection and estimation problems in heavy-tailed noise
Signal Processing - Signal processing with heavy-tailed models
Subspace fitting approaches for frequency estimation using real-valued data
IEEE Transactions on Signal Processing - Part II
Robust parameter estimation of a deterministic signal in impulsivenoise
IEEE Transactions on Signal Processing
Iterative frequency estimation by interpolation on Fourier coefficients
IEEE Transactions on Signal Processing
A subspace-based direction finding algorithm using fractional lowerorder statistics
IEEE Transactions on Signal Processing
On Asymptotic Normality of Nonlinear Least Squares for Sinusoidal Parameter Estimation
IEEE Transactions on Signal Processing
Asymptotic analysis of a fast algorithm for efficient multiple frequency estimation
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
A robust periodogram for high-resolution spectral analysis
Signal Processing
Robust time-frequency distributions with complex-lag argument
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Fast communication: Robust M-periodogram with dichotomous search
Signal Processing
Design of distribution independent noise filters with online PDF estimation
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
| Hi-index | 35.68 |
Accurate estimation of the amplitude and frequency parameters of sinusoidal signals from noisy observations is an important problem in many signal processing applications. In this paper, the problem is investigated under the assumption of non-Gaussian noise in general and Laplace noise in particular. It is proven mathematically that the maximum likelihood estimator derived under the condition of Laplace white noise is able to attain an asymptotic Cramér-Rao lower bound which is one half of that achieved by periodogram maximization and nonlinear least squares. It is also proven that when applied to non-Laplace situations, the Laplace maximum likelihood estimator, which may also be referred to as the nonlinear least-absolute-deviations estimator, can achieve an even higher statistical efficiency especially when the noise distribution has heavy tails. A computational procedure is proposed to overcome the difficulty of local extrema in the likelihood function. Simulation results are provided to validate the analytical findings.