Adaptive signal processing
Digital spectral analysis: with applications
Digital spectral analysis: with applications
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Speech enhancement as a realisation issue
Signal Processing - Signal processing with heavy-tailed models
Parameter estimation of multichannel autoregressive processes in noise
Signal Processing
Adaptive AR modeling in white Gaussian noise
IEEE Transactions on Signal Processing
A subspace approach to estimation of autoregressive parameters fromnoisy measurements
IEEE Transactions on Signal Processing
On the noise-compensated Yule-Walker equations
IEEE Transactions on Signal Processing
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The estimation of the parameters of an autoregressive process (AR) from noisy observations is still a challenging problem. In this paper, we propose to sequentially estimate both the signal and the parameters, avoiding a non-linear approach such as the extended Kalman filter. The method is based on two conditionally linked Kalman filters running in parallel. Once a new observation is available, the first filter uses the latest estimated AR parameters to estimate the signal, while the second filter uses the estimated signal to update the AR parameters. This approach can be viewed as a recursive instrumental variable-based method and hence has the advantage of providing consistent estimates of the parameters from noisy observations. A comparative study with existing algorithms illustrates the performances of the proposed method when the additive noise is either white or coloured.