Antenna Arrays Calibration Using Recursive Least Squares Adaptive Filtering Algorithms Based on Inverse QR Decomposition

Djigan, Victor I.; Kurganov, Vladislav V.

doi:10.1109/ewdts50664.2020.9224860

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…It is found that the stochastic gradient noise can be diminished through the learning process. According to the model of standard network in [6], [7], we assume that 𝕃 nodes in a network connected with Λ ∈ ℝ 𝕃×𝕃 . Λ 𝑘𝑙 ≥ 0 if nodes 𝑘 and 𝑙 are linked within a network, otherwise it is zero.…”

Section: Diffusion Adaptation Strategymentioning

confidence: 99%

Diffusion recursive least squares algorithm based on triangular decomposition

Prongnuch

Sitjongsataporn

Wiangtong

2023

IJECE

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<span lang="EN-US">In this paper, diffusion strategies used by QR-decomposition based on recursive least squares algorithm (DQR-RLS) and the sign version of DQR-RLS algorithm (DQR-sRLS) are introduced for distributed networks. In terms of the QR-decomposition method and Cholesky factorization, a modified Kalman vector is given adaptively with the help of unitary rotation that can decrease the complexity from inverse autocorrelation matrix to vector. According to the diffusion strategies, combine-then-adapt (CTA) and adapt-then-combine (ATC) based on DQR-RLS and DQR-sRLS algorithms are proposed with the combination and adaptation steps. To minimize the cost function, diffused versions of CTA-DQR-RLS, ATC-DQR-RLS, CTA-DQR-sRLS and ATC-DiQR-sRLS algorithms are compared. Simulation results depict that the proposed DQR-RLS-based and DQR-sRLS-based algorithms can clearly achieve the better performance than the standard combine-then-adapt-diffusion RLS (CTA-DRLS) and ATC-DRLS mechanisms.</span>

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Section: Diffusion Adaptation Strategymentioning

confidence: 99%