In this work, we present low-complexity variable forgetting factor (VFF) techniques for diffusion recursive least squares (DRLS) algorithms. Particularly, we propose low-complexity VFF-DRLS algorithms for distributed parameter and spectrum estimation in sensor networks. For the proposed algorithms, they can adjust the forgetting factor automatically according to the posteriori error signal. We ...

In this work, we propose a low-complexity variable forgetting factor (VFF) mechanism for recursive least square (RLS) algorithms in interference suppression applications. The proposed VFF mechanism employs an updated component related to the time average of the error correlation to automatically adjust the forgetting factor in order to ensure fast convergence and good tracking of the interferen...

A type of parameter estimation technique based on the linear integral filter (LIF) method, the least-absolute error with variable forgetting factor (LAE+VFF) estimation method, is proposed in this paper to estimate the railway wheelset parameters modelled as a time-varying continuous-time (C-T) system. The inputs to the parameter estimator are the control signal and the railway wheelset system ...

In this work, we propose a low-complexity variable forgetting factor (VFF) mechanism for recursive least square (RLS) algorithms in interference suppression applications. The proposed VFF mechanism employs an updated component related to the time average of the error correlation to automatically adjust the forgetting factor in order to ensure fast convergence and good tracking of the interferen...

In this paper, a recursive least squares (RLS) based blind adaptive beamforming algorithm that features a new variable forgetting factor (VFF) mechanism is presented. The beamformer is designed according to the constrained constant modulus (CCM) criterion, and the proposed adaptive algorithm operates in the generalized sidelobe canceler (GSC) structure. A detailed study of its operating propert...

In this paper, a recursive least squares (RLS) based blind adaptive beamforming algorithm that features a new variable forgetting factor (VFF) mechanism is presented. The beamformer is designed according to the constrained constant modulus (CCM) criterion, and the proposed adaptive algorithm operates in the generalized sidelobe canceler (GSC) structure. A detailed study of its operating propert...

In impulsive noise environment, most learning algorithms are encountered difficulty in distinguishing the nature of large error signal, whether caused by the impulse noise or model error. Consequently, they suffer from large misadjustment or otherwise slow convergence. A new nonlinear RLS (VFF-NRLS) adaptive algorithm with variable forgetting factor for FIR filter is introduced. In this algorit...

The recursive least squares (RLS) algorithm is well known for its good convergence property and small mean square error in stationary environments. However RLS using constant forgetting factor cannot provide satisfactory performance in time varying environments. In this seminar, three variable forgetting factor (VFF) adaptation schemes for RLS are presented in order to improve the tracking perf...

This paper proposes an autonomous vehicle trajectory tracking system that fully considers road friction. When intelligent drives at high speed on roads with different friction coefficients, the difficulty of its control lies in fast and accurate identification coefficients. Therefore, improved strategy is designed based traditional recursive least squares (RLS), which utilized for coefficient. ...

In this correspondence, the bit-error-rate (BER) performance evaluation of the space-time block-coded (STBC) communication systems using the numeric-variable-forgetting-factor (NVFF) least-squares (LS) channel estimator is presented. The polynomial channel paradigm is incorporated in LS algorithm in conjunction with NVFF to improve the channel tracking performance under the nonstationary wirele...