System identification: theory for the user
System identification: theory for the user
Identification of linear systems: a practical guideline to accurate modeling
Identification of linear systems: a practical guideline to accurate modeling
Recursive total least squares algorithm for image reconstruction from noisy, undersampled frames
Multidimensional Systems and Signal Processing
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
A fast recursive total least squares algorithm for adaptive FIR filtering
IEEE Transactions on Signal Processing - Part I
Bias removal in equation-error adaptive IIR filters
IEEE Transactions on Signal Processing
An unbiased equation error identifier and reduced-orderapproximations
IEEE Transactions on Signal Processing
An efficient recursive total least squares algorithm for FIRadaptive filtering
IEEE Transactions on Signal Processing
On a least-squares-based algorithm for identification of stochasticlinear systems
IEEE Transactions on Signal Processing
Localization of wideband signals using least-squares and totalleast-squares approaches
IEEE Transactions on Signal Processing
Error whitening criterion for adaptive filtering: theory and algorithms
IEEE Transactions on Signal Processing
QR-based TLS and mixed LS-TLS algorithms with applications to adaptive IIR filtering
IEEE Transactions on Signal Processing
Noisy FIR identification as a quadratic eigenvalue problem
IEEE Transactions on Signal Processing
| Hi-index | 0.08 |
The presence of contaminating noises in both the input and output of an FIR system usually results in a biased least squares (LS) parameter estimate. The total least squares (TLS) methods are known to be efficient in achieving unbiased estimation, if the ratio of the input noise variance to the output noise variance (NNR) is known. However, when the NNR is unknown, a simple analysis shows that the classical LS and TLS estimation methods usually have such insufficient degree of freedom that they can achieve the unbiased solution. In this paper, it is shown by analyzing the algebraic structure of the correlation matrix that the unbiased estimate of FIR parameters can be obtained by solving a special bilinear equation. Then we develop a bilinear equation method (BEM) for solving the bilinear equation associated with the unbiased solution of the FIR system or filtering under the unknown NNR. Unlike the available unbiased estimators, the main advantage is that the proposed method exploits much sufficiently the special structure of the correlation matrix and obtains much accurate estimation for FIR filtering in the presence of input and output noises. Two simulation examples are presented that show the good performance of the proposed method, including its superiority over the classical LS and TLS approaches, and the instrumental variable methods.