Name
Abstract | ||
|---|---|---|
Parametric estimation of phase-modulated signals (PMS) in additive white Gaussian noise is considered. The prohibitive computational expense of maximum likelihood estimation for this problem has led to the development of many suboptimal estimators which are relatively inaccurate and cannot operate at low signal-to-noise ratios (SNRs). In this paper, a novel technique based on a probabilistic unwrapping of the phase of the observations is developed. The method is capable of more accurate estimation and operates effectively at much lower SNRs than existing algorithms. This is demonstrated in Monte Carlo simulations. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSP.2009.2025801 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
novel technique,monte carlo simulation,low signal-to-noise ratio,maximum likelihood estimation,parameter estimation,bayesian unwrapping,phase-modulated signal,lower snrs,parametric estimation,probabilistic unwrapping,accurate estimation,additive white gaussian noise,monte carlo simulations,maximum likelihood estimate,signal processing,bayesian methods,monte carlo methods,awgn,convergence,phase modulation,polynomials,signal to noise ratio | Signal processing,Monte Carlo method,Mathematical optimization,Signal-to-noise ratio,Estimation theory,Probabilistic logic,Additive white Gaussian noise,Mathematics,Bayesian probability,Estimator | Journal |
Volume | Issue | ISSN |
57 | 11 | 1053-587X |
Citations | PageRank | References |
5 | 0.43 | 17 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark R. Morelande | 1 | 195 | 24.96 |