Regularized Autoregressive Multiple Frequency Estimation

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.

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Journal title

volume 10  issue None

pages  141- 166

publication date 2011-11

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