Abstract
This paper addresses the problem of multichannel autoregressive (MAR) parameter estimation in the presence of spatially correlated noise by steepest descent (SD) method which combines low-order and high-order Yule-Walker (YW) equations. In addition, to yield an unbiased estimate of the MAR model parameters, we apply inverse filtering for noise covariance matrix estimation. In a simulation study, the performance of the proposed unbiased estimation algorithm is evaluated and compared with existing parameter estimation methods.
Highlights
IntroductionThe noisy MAR modeling has many applications such as high resolution multichannel spectral estimation [1], parametric multichannel speech enhancement [2], MIMO-AR time varying fading channel estimation [3], and adaptive signal detection [4]
The noisy MAR modeling has many applications such as high resolution multichannel spectral estimation [1], parametric multichannel speech enhancement [2], MIMO-AR time varying fading channel estimation [3], and adaptive signal detection [4].When the noise-free observations are available, the Nuttall-Strand method [5], the maximum likelihood (ML) estimator [6], and the extension of some standard schemes in scalar case to multichannel can be used for estimation of MAR model parameters
The modified Yule-Walker (MYW) method is a conventional method for noisy MAR parameter estimation
Summary
The noisy MAR modeling has many applications such as high resolution multichannel spectral estimation [1], parametric multichannel speech enhancement [2], MIMO-AR time varying fading channel estimation [3], and adaptive signal detection [4]. The modified Yule-Walker (MYW) method is a conventional method for noisy MAR parameter estimation This method uses estimated correlation at lags beyond the AR order [1]. In [10] an improved LS (ILS) based method has been developed for estimation of noisy MAR signals In this method, bias correction is performed using observation noise covariance estimation. In the ILSV method, the channel noises can be correlated and no constraint is imposed on the covariance matrix of channel noises This method has poor convergence when the SNR is low. The performance of the proposed algorithm is evaluated and compared with that of the LS, MYW, ILSV, and ALSV methods in spatially correlated noise case. The convergence behaviour of the proposed method is better than those of the ILSV and the ALSV methods
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