A Decomposition-Based RLS Algorithm with Variable Forgetting Factors

The performance of the recursive least-squares (RLS) algorithm is mainly controlled by the forgetting factor. Using a constant value of this important parameter leads to a compromise between the main performance criteria, i.e., low misadjustment versus fast tracking. In this paper, we propose a vari...

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Vydáno v:2020 13th International Conference on Communications (COMM) s. 43 - 48
Hlavní autoři: Elisei-Iliescu, Camelia, Paleologu, Constantin, Benesty, Jacob, Stanciu, Cristian, Anghel, Cristian, Ciochina, Silviu
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2020
Témata:
Adaptive filter
Adaptive systems
Approximation algorithms
Convergence
echo cancellation
Echo cancellers
Indexes
Kronecker product decomposition
recursive least-squares (RLS) algorithm
Signal to noise ratio
Steady-state
variable forgetting factor
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Shrnutí:The performance of the recursive least-squares (RLS) algorithm is mainly controlled by the forgetting factor. Using a constant value of this important parameter leads to a compromise between the main performance criteria, i.e., low misadjustment versus fast tracking. In this paper, we propose a variable forgetting factor (VFF) solution applicable to the recently developed RLS algorithm based on the nearest Kronecker product decomposition (namely RLS-NKP). The RLS-NKP algorithm exploits an efficient decomposition of the impulse response, thus being suitable for the identification of long length systems (like echo paths). The resulting VFF-RLS-NKP algorithm inherits the performance features of its original counterpart, while also achieving improvements due to the VFF approach. Simulations performed in the context of echo cancellation support this behavior.
DOI:10.1109/COMM48946.2020.9141974