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Bayesian hypothesis testing detector for one bit diffusion LMS with blind missing samples

Zayyani, H ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1016/j.sigpro.2018.01.002
  3. Publisher: Elsevier B.V , 2018
  4. Abstract:
  5. This paper proposes a sparse distributed estimation algorithm when missing data occurs in the measurements over adaptive networks. Two classes of measurement models are considered. First, the traditional linear regression model is investigated and second the sign of the linear regression model is studied. The latter is referred to as one-bit model. We utilize the diffusion LMS strategy, in the proposed methods, where a set of nodes cooperates with each other to estimate a vector model parameter. In both models, it is shown that replacing the missing sample with a simple estimate is equivalent to removing the missing sample from the distributed diffusion algorithm. We consider two cases, where in the first case the positions of missing samples are known (non-blind) and in the second case the positions of missing samples are unknown (blind). In the linear regression model scenario, a Bayesian hypothesis testing (BHT) is used for detection of the missing samples. In the one-bit model, in addition to BHT detector, a simple heuristic detector, based on mean square error (MSE), is also suggested. Simulation results show the effectiveness of the proposed detector-assisted distributed algorithms. © 2018 Elsevier B.V
  6. Keywords:
  7. Bayesian hypothesis testing detector ; Diffusion strategy ; Missing samples ; One bit ; Diffusion ; Mean square error ; Regression analysis ; Statistical tests ; Adaptive networks ; Bayesian hypothesis ; Diffusion algorithm ; Diffusion LMS ; Diffusion strategies ; Distributed estimation ; Linear regression models ; Measurement model ; Linear regression
  8. Source: Signal Processing ; Volume 146 , May , 2018 , Pages 61-65 ; 01651684 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0165168418300021