Parameter estimation of phase-modulated signals using Bayesian unwrapping

Authors:
Mark R. Morelande
Affiliations:
Melbourne Systems Laboratory, Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville, Victoria, Australia
Venue:
IEEE Transactions on Signal Processing
Year:
2009

Citing 13
Cited 1

Estimating the frequency of a noisy sinusoid by linear regression

IEEE Transactions on Information Theory
A polynomial phase parameter estimation-phase unwrapping algorithm

ICASSP '94 Proceedings of the Acoustics, Speech, and Signal Processing,1994. on IEEE International Conference - Volume 04
Least squares estimation of polynomial phase signals via stochastic tree-search

ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
A new class of multilinear functions for polynomial phase signal analysis

IEEE Transactions on Signal Processing
A modification of the discrete polynomial transform

IEEE Transactions on Signal Processing
On the performance of cyclic moments-based parameter estimators ofamplitude modulated polynomial phase signals

IEEE Transactions on Signal Processing
Instantaneous frequency estimation of polynomial FM signals usingthe peak of the PWVD: statistical performance in the presence ofadditive gaussian noise

IEEE Transactions on Signal Processing
Multicomponent polynomial phase signal analysis using a trackingalgorithm

IEEE Transactions on Signal Processing
Polynomial phase signal analysis based on the polynomialderivatives decompositions

IEEE Transactions on Signal Processing
Aliasing of polynomial-phase signal parameters

IEEE Transactions on Signal Processing
Product high-order ambiguity function for multicomponentpolynomial-phase signal modeling

IEEE Transactions on Signal Processing
Estimating signal parameters using the nonlinear instantaneousleast squares approach

IEEE Transactions on Signal Processing
A fast algorithm for estimating the parameters of a quadratic FM signal

IEEE Transactions on Signal Processing

Fast communication: On the Cramér-Rao bound for polynomial phase signals

Signal Processing

Quantified Score

Hi-index	35.68

Visualization

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.