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AR modeling is used in a wide range of applications from speech processing to Rayleigh fading channel simulation. When the observations are disturbed by an additive white noise, the standard least squares estimation of the AR parameters is biased. Some authors of this paper recently reformulated this problem as an errors-in-variables (EIV) issue and proposed an off-line solution, which outperforms...
Space Time Adaptive Processing (STAP) is known as one of the key techniques for radar processing, but its computational cost is high. To reduce it, the so-called STAP filters based on adaptive 2D FIR filters consists in modeling the clutter-plus-noise as a multichannel autoregressive (N-AR) process. In this paper, our contribution is twofold. 1/ the secondary data are assumed to be modeled as a N-AR...
In various applications from radar processing to mobile communication systems based on CDMA for instance, M-AR multichannel processes are often considered and may be combined with Kalman filtering. However, the estimations of the M-AR parameter matrices and the covariance matrices of the additive noise and the driving process from noisy observations are key issues to be addressed. In this paper, we...
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