In this paper, we develop a fast recursive algorithm with a view to finding the total least squares (TLS) solution for adaptive FIR filtering with input and output noises. We introduce an approximate inverse power iteration in combination with Galerkin method so that the TLS solution can be updated adaptively at a lower computational cost. We further reduce the computational complexity of the developed algorithm by making efficient computation of the fast gain vector. We then make a careful investigation into global convergence of the developed algorithm. Simulation results are provided that clearly illustrate appealing performances of the developed algorithm
Abstract—In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm is base...
This paper is concerned with identifying parameters of finite impulse response (FIR) systems from no...
“System identification using the adaptive filter is commonly utilized in areas such as noise cancell...
The presence of contaminating noises at both the input and the output of an finite-impulse-response ...
This paper proposes a new fast recursive total least squares (N-RTLS) algorithm to recursively compu...
This paper considers the problem of adaptive identification of IIR systems when the system output is...
Abstract: — The numerically stable version of fast recursive least squares (NS-FRLS) algorithms rep...
This work develops a new fast recursive total least squares (N-RTLS) algorithm to recursively comput...
DoctorAdaptive filters have been used in a wide variety of applications such as noise cancellation, ...
This brief proposes an approach to apply least-squares techniques to adaptive FIR filtering in casca...
In this paper, we propose a new adaptive filtering algorithm for system identification. The algorith...
Among many adaptive algorithms that exist in the open literature, the class of approaches which are ...
Abstract: In this paper, we present a new version of numerically stable fast recursive least squares...
A unified approach for generating fast block- and sequential-gradient LMS FIR (least mean square fin...
A unified approach for generating fast block- and sequential-gradient LMS FIR (least mean square fin...
Abstract—In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm is base...
This paper is concerned with identifying parameters of finite impulse response (FIR) systems from no...
“System identification using the adaptive filter is commonly utilized in areas such as noise cancell...
The presence of contaminating noises at both the input and the output of an finite-impulse-response ...
This paper proposes a new fast recursive total least squares (N-RTLS) algorithm to recursively compu...
This paper considers the problem of adaptive identification of IIR systems when the system output is...
Abstract: — The numerically stable version of fast recursive least squares (NS-FRLS) algorithms rep...
This work develops a new fast recursive total least squares (N-RTLS) algorithm to recursively comput...
DoctorAdaptive filters have been used in a wide variety of applications such as noise cancellation, ...
This brief proposes an approach to apply least-squares techniques to adaptive FIR filtering in casca...
In this paper, we propose a new adaptive filtering algorithm for system identification. The algorith...
Among many adaptive algorithms that exist in the open literature, the class of approaches which are ...
Abstract: In this paper, we present a new version of numerically stable fast recursive least squares...
A unified approach for generating fast block- and sequential-gradient LMS FIR (least mean square fin...
A unified approach for generating fast block- and sequential-gradient LMS FIR (least mean square fin...
Abstract—In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm is base...
This paper is concerned with identifying parameters of finite impulse response (FIR) systems from no...
“System identification using the adaptive filter is commonly utilized in areas such as noise cancell...