  This paper describes several new algorithms for estimating the parameters of
a periodic bandlimited signal from samples corrupted by jitter (timing noise)
and additive noise. Both classical (non-random) and Bayesian formulations are
considered: an Expectation-Maximization (EM) algorithm is developed to compute
the maximum likelihood (ML) estimator for the classical estimation framework,
and two Gibbs samplers are proposed to approximate the Bayes least squares
(BLS) estimate for parameters independently distributed according to a uniform
prior. Simulations are performed to demonstrate the significant performance
improvement achievable using these algorithms as compared to linear estimators.
The ML estimator is also compared to the Cramer-Rao lower bound to determine
the range of jitter for which the estimator is approximately efficient. These
simulations provide evidence that the nonlinear algorithms derived here can
tolerate 1.4-2 times more jitter than linear estimators, reducing on-chip ADC
power consumption by 50-75 percent.
