Regressor-based adaptive infinite impulse response filtering

Superior performance of fast recursive least squares (RLS) algorithms over the descent-type least mean square (LMS) algorithms in the adaptation of FIR systems has not been realized in the adaptation of IIR systems. This is the result of having noisy observations of the original system output resulting in significantly biased estimates of the system parameters when this noisy signal is used in the adaptive system. Here, we propose an adaptive IIR system structure consisting of two parts: a two-channel FIR adaptive filter whose parameters are updated by the rotation-based multichannel least squares lattice (QR-MLSL) algorithm, and an adaptive régresser that provides more reliable estimates to the original system output based on previous values of the adaptive system output and noisy observation of the original system output. Two different regressors are investigated, and robust ways of adaptation of the régresser parameters are proposed. The performances of the proposed algorithms are compared with composite régresser (CR) and bias remedy least mean square equation error (BRLE) algorithms that are LMS-type successful adaptation algorithms, and it is found that in addition to the expected convergence speedup, the proposed algorithms provide better estimates to the system parameters at low SNR value. In addition, the extended Kaiman filtering approach is tailored to our application. Comparison of the proposed regressor-based algorithms with the extended Kaiman filter approach revealed that the proposed approaches provide improved estimates in systems with abrupt parameter changes. ©1993 IEEE

Regressor based adaptive infinite impulse response filtering

Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 35-36.Acar, EmrahM.S

Regressor Based Adaptive Infinite Impulse Response Filtering

To take advantage of fast converging multi--channel recursive least squares algorithms, we propose an adaptive IIR system structure consisting of two parts: a two--channel FIR adaptive filter whose parameters are updated by rotation-- based multi--channel least squares lattice (QR--MLSL) algorithm, and an adaptive regressor which provides more reliable estimates to the original system output based on previous values of the adaptive system output and noisy observation of the original system output. Two different regressors are investigated and robust ways of adaptation of the regressor parameters are proposed. Based on extensive set of simulations, it is shown that the proposed algorithms converge faster to more reliable parameter estimates than LMS type algorithms. 1. THE REGRESSOR BASED IIR ADAPTIVE FILTER STRUCTURE As shown in Fig. 1, in a typical adaptive filtering application, input, x(n), and noisy output, d(n), of an unknown system are available for processing by an adaptive sys..

Effective Algorithms For Regressor Based Adaptive Infinite Impulse Response Filtering

To take advantage of fast converging multi--channel recursive least squares algorithms, we propose an adaptive IIR system structure consisting of two parts: a two--channel FIR adaptive filter whose parameters are updated by rotation-- based multi--channel least squares lattice (QR--MLSL) algorithm, and an adaptive regressor which provides more reliable estimates to the original system output based on previous values of the adaptive system output and noisy observation of the original system output. Two different regressors are investigated and robust ways of adaptation of the regressor parameters are proposed. Based on extensive set of simulations, it is shown that the proposed algorithms converge faster to more reliable parameter estimates than LMS type algorithms. 1. THE REGRESSOR BASED IIR ADAPTIVE FILTER STRUCTURE As shown in Fig. 1, in a typical adaptive filtering application, input, x(n), and noisy output, d(n), of an unknown system are available for processing by an adaptive sy..