Authors

• Bei Chen
• Yulia R. Gel

Abstract

The paper addresses a problem of tracking multiple number
of frequencies using Regularized Autoregressive (RAR) approximation.
The RAR procedure allows to decrease approximation bias, comparing
to other AR-based frequency detection methods, while still providing
competitive variance of sample estimates. We show that the RAR estimates
of multiple periodicities are consistent in probability and illustrate
dynamics of RAR in respect to sample size and signal-to-noise ration by
simulations.

Keywords

• Autoregressive approximation
• frequency tracking
• least squares method
• periodic time series
• Regularization