Efficient Algorithms for Linear System Identification with Particular Symmetric Filters
نویسندگان
چکیده
In linear system identification problems, it is important to reveal and exploit any specific intrinsic characteristic of the impulse responses, in order improve overall performance, especially terms accuracy complexity solution. this paper, we focus on nearest Kronecker product decomposition together with low-rank approximations. Such an approach suitable for a wide range real-world systems. Most importantly, reformulate problem by using particular symmetric filter within development, which allows us efficiently design two (iterative/recursive) algorithms. First, iterative Wiener proposed, improved performance as compared conventional filter, challenging conditions (e.g., small amount available data and/or noisy environments). Second, even more practical solution developed, form recursive least-squares adaptive algorithm, could represent appealing choice real-time applications. Overall, based proposed approach, that can be conventionally solved L=L1L2 equations (with L unknown parameters) reformulated combination systems PL1 PL2 equations, respectively, where usually P≪L2 (i.e., total PL1+PL2 parameters). This lead advantages, both complexity. Simulation results are provided framework network acoustic echo cancellation, supporting gain features
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12094263