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Tensor-Based Adaptive Filtering Algorithms
| Content Provider | MDPI |
|---|---|
| Author | Silviu, Ciochină Dogariu, Laura-Maria Stanciu, Cristian-Lucian Elisei-Iliescu, Camelia Paleologu, Constantin Benesty, Jacob |
| Copyright Year | 2021 |
| Description | Tensor-based signal processing methods are usually employed when dealing with multidimensional data and/or systems with a large parameter space. In this paper, we present a family of tensor-based adaptive filtering algorithms, which are suitable for high-dimension system identification problems. The basic idea is to exploit a decomposition-based approach, such that the global impulse response of the system can be estimated using a combination of shorter adaptive filters. The algorithms are mainly tailored for multiple-input/single-output system identification problems, where the input data and the channels can be grouped in the form of rank-1 tensors. Nevertheless, the approach could be further extended for single-input/single-output system identification scenarios, where the impulse responses (of more general forms) can be modeled as higher-rank tensors. As compared to the conventional adaptive filters, which involve a single (usually long) filter for the estimation of the global impulse response, the tensor-based algorithms achieve faster convergence rate and tracking, while also providing better accuracy of the solution. Simulation results support the theoretical findings and indicate the advantages of the tensor-based algorithms over the conventional ones, in terms of the main performance criteria. |
| Starting Page | 481 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym13030481 |
| Journal | Symmetry |
| Issue Number | 3 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-15 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Automotive Engineering Industrial Engineering Adaptive Filters Least-mean-square (lms) Algorithm Recursive Least-squares (rls) Algorithm System Identification Tensor Decomposition |
| Content Type | Text |
| Resource Type | Article |
