Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Rastegarnia, Amir"'
Naturally complex-valued information or those presented in complex domain are effectively processed by an augmented complex least-mean-square (ACLMS) algorithm. In some applications, the ACLMS algorithm may be too computationally- and memory-intensiv
Externí odkaz:
http://arxiv.org/abs/2001.08981
Akademický článek
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In conventional distributed Kalman filtering, employing diffusion strategies, each node transmits its state estimate to all its direct neighbors in each iteration. In this paper we propose a partial diffusion Kalman filter (PDKF) for state estimation
Externí odkaz:
http://arxiv.org/abs/1705.08920
Partial diffusion-based recursive least squares (PDRLS) is an effective method for reducing computational load and power consumption in adaptive network implementation. In this method, each node shares a part of its intermediate estimate vector with
Externí odkaz:
http://arxiv.org/abs/1607.05539
In this paper we consider the problem of decentralized (distributed) adaptive learning, where the aim of the network is to train the coefficients of a widely linear autoregressive moving average (ARMA) model by measurements collected by the nodes. Su
Externí odkaz:
http://arxiv.org/abs/1606.05777
In this letter we focus on designing self-organizing diffusion mobile adaptive networks where the individual agents are allowed to move in pursuit of an objective (target). The well-known Adapt-then-Combine (ATC) algorithm is already available in the
Externí odkaz:
http://arxiv.org/abs/1603.08543
Partial diffusion scheme is an effective method for reducing computational load and power consumption in adaptive network implementation. The Information is exchanged among the nodes, usually over noisy links. In this paper, we consider a general ver
Externí odkaz:
http://arxiv.org/abs/1511.09044
Autor:
Khalili, Azam, Rastegarnia, Amir
We study the effect of fading in the communication channels between sensor nodes on the performance of the incremental least mean square (ILMS) algorithm, and derive steady state performance metrics, including the mean-square deviation (MSD), excess
Externí odkaz:
http://arxiv.org/abs/1509.02664
Autor:
Khalili, Azam, Rastegarnia, Amir
We study the effect of fading in the communication channels between nodes on the performance of the incremental least mean square (ILMS) algorithm. We derive steady-state performance metrics, including the mean-square deviation (MSD), excess mean-squ
Externí odkaz:
http://arxiv.org/abs/1508.02108
In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based
Externí odkaz:
http://arxiv.org/abs/1507.06672