Diffusion Bayesian Decorrelation Algorithms Over Networks

Huang, Fuyi; Zhang, Sheng; Zhang, Jia-shu; Chen, Hongyang; Sayed, Ali H.

doi:10.1109/tsp.2023.3248484

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…In different direction of research, some papers deal with the correlated input cases for distributed estimation [23], [24]. The correlated input signal makes the algorithm converge slowly.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, [23] uses two convex-combined decorrelated transversal filter which doubly complicates the scheme. Moreover, in a recent work [24], a class of diffusion Bayesian decorrelation least mean squares algorithms are presented based on decorrelated observation models. Again, the Bayesian nature of decorrelation method utilized in this paper is complex to implement in a sensor network in which the size and limited computational complexity is a requisite.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hidden Markov Model Decorrelated Diffusion Leaky LMS With Optimized Leaky Factor

Zayyani,

Salman,

Korki

et al. 2024

IEEE Access

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In this paper, two basic novel modifications of Diffusion Leaky Least Mean Square (DL-LMS) algorithm are proposed. First, the optimized leaky factor is obtained in closed form using the minimum disturbance principle, thanks to quadratic form of disturbance. Then, a low complexity decorrelated version of DL-LMS is presented based on the statistical Hidden Markov Modeling (HMM) of time correlations of input signals. The decorrelation is performed using a simple one-tap filter. Simulation results demonstrate the effectiveness of both modifications in comparison to other methods. INDEX TERMS Distributed estimation, leaky LMS, hidden Markov model, diffusion.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%