@InProceedings{6952110,
  author = {H. Ijima and R. Diversi and E. Grivel},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Iterative approach to estimate the parameters of a TVAR process corrupted by a MA noise},
  year = {2014},
  pages = {456-460},
  abstract = {A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TVAR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (ILS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. Simulation studies illustrate the advantages and the limits of the approach.},
  keywords = {autoregressive processes;iterative methods;least squares approximations;moving average processes;signal processing;AR parameters;improved ILS methods;improved least-squares methods;moving average process;additive colored measurement noise;deterministic regression methods;additive zero-mean white noise;time-varying autoregressive parameter tracking;MA noise;TVAR process;iterative approach;Noise;Abstracts;Indexes;Noise measurement;Kalman filters;Time-varying autoregressive model;unbiased parameter estimation;colored noise;moving average process;deterministic regression approach},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569923795.pdf},
}

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Grivel},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Iterative approach to estimate the parameters of a TVAR process corrupted by a MA noise},\n year = {2014},\n pages = {456-460},\n abstract = {A great deal of interest has been paid to the time-varying autoregressive (TVAR) parameter tracking, but few papers deal with this issue when noisy observations are available. Recently, this problem was addressed for a TVAR process disturbed by an additive zero-mean white noise, by using deterministic regression methods. In this paper, we focus our attention on the case of an additive colored measurement noise modeled by a moving average process. More particularly, we propose to estimate the TVAR parameters by using a variant of the improved least-squares (ILS) methods, initially introduced by Zheng to estimate the AR parameters from a signal embedded in a white noise. 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