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Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, a sparsity inducing weighted l.............. 1.............. norm penalty was added to the RLS cost function to improve the adaptive identification of sparse systems, which achieved faster convergence than standard RLS when the system under consideration is sparse.
Abstract: The authors propose a new approach for the adaptive identification of sparse systems. This approach improves on the recursive least squares (RLS) algorithm by adding a sparsity inducing weighted l 1 norm penalty to the RLS cost function. Subgradient analysis is utilised to develop the recursive update equations for the calculation of the optimum system estimate, which minimises the regularised cost function. Two new algorithms are introduced by considering two different weighting scenarios for the l 1 norm penalty. These new l 1 relaxation-based RLS algorithms emphasise sparsity during the adaptive filtering process, and they allow for faster convergence than standard RLS when the system under consideration is sparse. The authors test the performance of the novel algorithms and compare it with standard RLS and other adaptive algorithms for sparse system identification.

53 citations

Journal ArticleDOI
TL;DR: A new FIR adaptive filtering algorithm based on the Quasi-Newton (QN) optimization algorithm that uses a variable step-size in the coefficient update equation that leads to an improved performance.

53 citations

Journal ArticleDOI
TL;DR: Improved central difference transform Kalman filter method based on square root second-ordercentral difference transform (SRCDKF) was utilized for real-time estimation of SOC in LIBs and the results look promising for future BMS SOC estimation in practice.

53 citations

Journal ArticleDOI
TL;DR: A new prediction error method (PEM) based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback path and the nonstationary speech source model and is superior to earlier approaches whenever long acoustic channels are dealt with.
Abstract: While several proactive acoustic feedback (Larsen-effect) cancellation schemes have been presented for speech applications with short acoustic feedback paths as encountered in hearing aids, these schemes fail with the long impulse responses inherent to, for instance, public address systems. We derive a new prediction error method (PEM)-based scheme (referred to as PEM-AFROW) which identifies both the acoustic feedback path and the nonstationary speech source model. A cascade of a short- and a long-term predictor removes the coloring and periodicity in voiced speech segments, which account for the unwanted correlation between the loudspeaker signal and the speech source signal. The predictors calculate row operations which are applied to prewhiten the speech source signal, resulting in a least squares system that is solved recursively by means of normalized least mean square or recursive least squares algorithms. Simulations show that this approach is indeed superior to earlier approaches whenever long acoustic channels are dealt with

53 citations

Journal ArticleDOI
TL;DR: The purpose of this paper is to derive a closed-form solution in the sense of blind equalization and it will be shown that the equalizer coefficients can be uniquely derived from the eigenvectors of a specific 4th-order cumulant matrix of the received signal.

53 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022104
2021172
2020228
2019234
2018237