Special Issue "Adaptive Filtering Algorithms"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editor

Guest Editor
Prof. Dr. Felix Albu

Department of Electronics, Valahia University of Targoviste, 130082 Targoviste, Romania
Website | E-Mail
Interests: adaptive filtering; active noise control; adaptive signal processing; image enhancement

Special Issue Information

Dear Colleagues,

Adaptive filters are an important component of many signal processing, communication or computing systems. The main (but not exclusive) theme of this Special Issue is adaptive filtering algorithms for various system identification applications, such as echo cancellation, active noise control, hearing aids, channel estimation, etc. Low complexity or sparsity-aware adaptive algorithm implementations for speech and image applications are also envisaged. Since the application areas are becoming wider with the development of mobile devices, the importance of the robustness of the adaptive algorithms in adverse environments is also addressed. Therefore, advanced linear and non-linear approaches are needed to design adaptive algorithms that make use of last generation architectures and efficient computing approaches.

Prof. Dr. Felix Albu
Guest Editor

Manuscript Submission Information

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Keywords

  • Recent developments in adaptive filtering algorithms
  • Adaptive system identification and channel modelling
  • Image and speech enhancement and processing using adaptive filters
  • Active noise control and echo reduction using adaptive algorithms
  • Adaptive filters for wireless system designs
  • Sparsity-aware adaptive algorithms and their efficient implementation

Published Papers (3 papers)

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Research

Open AccessArticle On Fast Converging Data-Selective Adaptive Filtering
Algorithms 2019, 12(1), 4; https://doi.org/10.3390/a12010004
Received: 30 November 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
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Abstract
The amount of information currently generated in the world has been increasing exponentially, raising the question of whether all acquired data is relevant for the learning algorithm process. If a subset of the data does not bring enough innovation, data-selection strategies can be
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The amount of information currently generated in the world has been increasing exponentially, raising the question of whether all acquired data is relevant for the learning algorithm process. If a subset of the data does not bring enough innovation, data-selection strategies can be employed to reduce the computational complexity cost and, in many cases, improve the estimation accuracy. In this paper, we explore some adaptive filtering algorithms whose characteristic features are their fast convergence and data selection. These algorithms incorporate a prescribed data-selection strategy and are compared in distinct applications environments. The simulation results include both synthetic and real data. Full article
(This article belongs to the Special Issue Adaptive Filtering Algorithms)
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Open AccessArticle Steady-State Performance of an Adaptive Combined MISO Filter Using the Multichannel Affine Projection Algorithm
Algorithms 2019, 12(1), 2; https://doi.org/10.3390/a12010002
Received: 3 December 2018 / Revised: 12 December 2018 / Accepted: 14 December 2018 / Published: 20 December 2018
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Abstract
The combination of adaptive filters is an effective approach to improve filtering performance. In this paper, we investigate the performance of an adaptive combined scheme between two adaptive multiple-input single-output (MISO) filters, which can be easily extended to the case of multiple outputs.
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The combination of adaptive filters is an effective approach to improve filtering performance. In this paper, we investigate the performance of an adaptive combined scheme between two adaptive multiple-input single-output (MISO) filters, which can be easily extended to the case of multiple outputs. In order to generalize the analysis, we consider the multichannel affine projection algorithm (APA) to update the coefficients of the MISO filters, which increases the possibility of exploiting the capabilities of the filtering scheme. Using energy conservation relations, we derive a theoretical behavior of the proposed adaptive combination scheme at steady state. Such analysis entails some further theoretical insights with respect to the single channel combination scheme. Simulation results prove both the validity of the theoretical steady-state analysis and the effectiveness of the proposed combined scheme. Full article
(This article belongs to the Special Issue Adaptive Filtering Algorithms)
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Open AccessArticle A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms
Algorithms 2018, 11(12), 211; https://doi.org/10.3390/a11120211
Received: 1 December 2018 / Revised: 13 December 2018 / Accepted: 14 December 2018 / Published: 17 December 2018
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Abstract
The system identification problem becomes more challenging when the parameter space increases. Recently, several works have focused on the identification of bilinear forms, which are related to the impulse responses of a spatiotemporal model, in the context of a multiple-input/single-output system. In this
[...] Read more.
The system identification problem becomes more challenging when the parameter space increases. Recently, several works have focused on the identification of bilinear forms, which are related to the impulse responses of a spatiotemporal model, in the context of a multiple-input/single-output system. In this framework, the problem was addressed in terms of the Wiener filter and different basic adaptive algorithms. This paper studies two types of algorithms tailored for the identification of such bilinear forms, i.e., the Kalman filter (along with its simplified version) and an optimized least-mean-square (LMS) algorithm. Also, a comparison between them is performed, which shows interesting similarities. In addition to the mathematical derivation of the algorithms, we also provide extensive experimental results, which support the theoretical findings and indicate the good performance of the proposed solutions. Full article
(This article belongs to the Special Issue Adaptive Filtering Algorithms)
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