Citation
Abstract
Motivated by advances in signal processing technology that support more complex algorithms, researchers have taken a new look at the problem of estimating the phase and other parameters of a nearly periodic waveform in additive Gaussian noise, based on observation during a given time interval. In Part I, the general problem was introduced and the maximum a posteriori probability criterion with signal space interpretation was used to obtain the structures of optimum and some suboptimum phase estimators for known constant frequency and unknown phase with an a priori distribution. In Part II, optimal algorithms were obtained for some cases where the phase (and frequency) is a parameterized function of time with the unknown parameters having a joint a priori distribution. The intrinsic and extrinsic geometry of hypersurfaces was introduced to provide insight to the estimation problem for the small-noise and large-noise cases. In Part III, the actual performances of some of the highly nonlinear estimation algorithms of Parts I and II are evaluated by numerical simulation using Monte Carlo techniques.
Details
- Volume
- 42-104
- Published
- February 15, 1991
- Pages
- 157–160
- File Size
- 155.9 KB