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Diffusion strategies for distributed Kalman filtering: formulation and performance analysis

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Performance of diffusion adaptation for collaborative optimization

Ali H. Sayed

We derive an adaptive diffusion mechanism to optimize global cost functions in a distributed manner over a network of nodes. The cost function is assumed to consist of the sum of individual components, and diffusion adaptation is used to enable the nodes t ...

IEEE2012

Diffusion Bias-Compensated RLS Estimation Over Adaptive Networks

Ali H. Sayed

We study the problem of distributed least-squares estimation over ad hoc adaptive networks, where the nodes have a common objective to estimate and track a parameter vector. We consider the case where there is stationary additive colored noise on both the ...

IEEE2011

Performance limits of LMS-based adaptive networks

Ali H. Sayed

In this work we analyze the mean-square performance of different strategies for adaptation over two-node least-mean-squares (LMS) networks. The results highlight some interesting properties for adaptive networks in comparison to centralized solutions. The ...

IEEE2011

Learning Nonlinear Multivariate Dynamics of Motion in Robotic Manipulators [accepted]

Aude Billard, Seyed Mohammad Khansari Zadeh, Elena Gribovskaya

Motion imitation requires reproduction of a dynamical signature of a movement, i.e. a robot should be able to encode and reproduce a particular path together with a specific velocity and/or an acceleration profile. Furthermore, a human provides only few de ...

SAGE Publications2011

Diffusion Strategies for Distributed Kalman Filtering and Smoothing

Ali H. Sayed

We study the problem of distributed Kalman filtering and smoothing, where a set of nodes is required to estimate the state of a linear dynamic system from in a collaborative manner. Our focus is on diffusion strategies, where nodes communicate with their d ...

IEEE2010

Distributed nonlinear Kalman filtering with applications to wireless localization

Ali H. Sayed

We study the problem of distributed state-space estimation, where a set of nodes are required to estimate the state of a nonlinear state-space system based on their observations. We extend our previous work on distributed Kalman filtering to the nonlinear ...

IEEE2010

Recursive Implementation of the Distributed Karhunen-Loeve Transform

Michael Christoph Gastpar

In the distributed linear source coding problem, a set of distributed sensors observe subsets of a data vector with noise, and provide the fusion center linearly encoded data. The goal is to determine the encoding matrix of each sensor such that the fusion ...

2010

Randomized incremental protocols over adaptive networks

Ali H. Sayed

We introduce an incremental cooperation mode into the framework of adaptive networks (AN). The method applies to generic topologies and avoids the need to establish a Hamiltonian cycle over the network, generalizing the original incremental mode, while kee ...

IEEE2010

Diffusion distributed Kalman filtering with adaptive weights

Ali H. Sayed

We study the problem of distributed Kalman filtering, where a set of nodes are required to collectively estimate the state of a linear dynamic system from their measurements. In diffusion Kalman filtering strategies, neighboring state estimates are linearl ...

IEEE2009

A Simple Alternative Derivation of the Expectation Correction Algorithm

Bertrand Mesot

The switching linear dynamical system (SLDS) is a popular model in time-series analysis. However, the complexity of inferring the state of the latent variables scales exponentially with the length of the time-series, resulting in many approximation strateg ...

2009