Robust Distributed Estimation by Networked Agents

Al-Sayed, Sara; Zoubir, Abdelhak M.; Sayed, Ali H.

doi:10.1109/tsp.2017.2703664

Cited by 50 publications

(24 citation statements)

References 41 publications

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“…We employ the network mean square deviation (MSD) to assess the performance of the algorithm, i.e., MSDnet(i) = 1 N N k=1 E{ w o − w k,i 2 2 }, where E{·} denotes the expectation. Usually, the impulsive noise can be described by either the Bernoulli-Gaussian (BG) distribution [16,17,18] or the α-Stable distribution [45,27]. We consider both cases.…”

Section: Simulation Resultsmentioning

confidence: 99%

Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents

Zhao

Lamare

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This work develops a robust diffusion recursive least squares algorithm to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. This algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate estimate update at each node. With the help of side information, the constraint is recursively updated in a diffusion strategy. Moreover, a control strategy for resetting the constraint is also proposed to retain good tracking capability when the estimated parameters suddenly change. Simulations show the superiority of the proposed algorithm over previously reported techniques in various impulsive noise scenarios.

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Section: Simulation Resultsmentioning

confidence: 99%

Robust Diffusion Recursive Least Squares Estimation with Side Information for Networked Agents

Zhao

Lamare

et al. 2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…In this paper, we suggest that E{ξ k } and E{ζ k } are obtained by the ensemble average using simulations. Consequently, although (49) is a semi-analytic result, it can also be used to evaluate the convergence of the proposed algorithm.…”

Section: B Analysis Of Evolution Behaviormentioning

confidence: 99%

“…Nevertheless, their main limitation is slow convergence especially when the nodes' input signals are colored (highly correlated). As shown in [49], the dEN algorithm converges slower than the dSE-LMS algorithm. In [54], by resorting to the adaptive projected subgradient method, a robust diffusion algorithm was developed which projects the output errors onto halfspaces defined by Huber's error function at each node, thereby speeding up the convergence.…”

Section: Introductionmentioning

confidence: 96%

Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks

Zhao

Lamare

et al. 2019

IEEE Trans. Signal Process.

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This work develops robust diffusion recursive least squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.

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“…To handle outliers in the desired data, many robust techniques based on robust statistics have been reported in the literature. The techniques based on Wilcoxon norm [25–29 ], Huber loss [30–32 ], error non‐linearity [33, 34 ], Lorentzian norm [35 ], maximum correntropy criterion [36 ], the least logarithmic absolute difference [37 ], least mean p‐power [38, 39 ], minimum disturbance [40 ] and mixed p ‐norm [41, 42 ] are found to be robust against outliers in the desired data. However, all these methods assume that the input data is uncontaminated, which may not be true in the practical scenarios.…”

Section: Introductionmentioning

confidence: 99%