Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
A method for estimating the coefficients of a polynomial phase signal
Signal Processing
Parameter estimation of phase-modulated signals using Bayesian unwrapping
IEEE Transactions on Signal Processing
Identifiability and aliasing in polynomial-phase signals
IEEE Transactions on Signal Processing
The Cramer-Rao lower bound for signals with constant amplitude andpolynomial phase
IEEE Transactions on Signal Processing
The discrete polynomial-phase transform
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
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Polynomial-phase signals have applications including radar, sonar, biology, and radio communication. Of practical importance is the estimation of the parameters of a polynomial phase signal from a sequence of noisy observations. Assuming that the noise is additive and Gaussian, the direct evaluation of the Cramer-Rao lower bound for this estimation problem involves evaluating the inverse of a matrix. Computing this inverse is numerically difficult for polynomial phase signals of large order. By making use of a family of orthogonal polynomials, we derive formulae for the Cramer-Rao bounds that are numerically stable and easy to compute.