Distributed Recursive Least-Squares for Consensus-Based In-Network Adaptive Estimation

Mateos, Gonzalo; Schizas, Ioannis D.; Giannakis, Georgios B.

doi:10.1109/tsp.2009.2024278

Cited by 131 publications

(141 citation statements)

References 7 publications

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“…References [15], [16] propose diffusion type LMS and RLS algorithms for distributed estimation; references [17], [18] propose algorithms for distributed estimation based on the alternating direction method of multipliers.…”

Section: Introductionmentioning

confidence: 99%

Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis

Bajović

Jakovetić

Xavier

et al. 2011

IEEE Trans. Signal Process.

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We study, by large deviations analysis, the asymptotic performance of Gaussian running consensus based distributed detection over random networks; in other words, we determine the exponential decay rate of the detection error probability. With running consensus, at each time step, each sensor averages its decision variable with the neighbors decision variables and accounts on-the-fly for its new observation.We show that: 1) when the rate of network information flow (the speed of averaging) is above a threshold, then Gaussian running consensus is asymptotically equivalent to the optimal centralized detector, i.e., the exponential decay rate of the error probability for running consensus equals the Chernoff information;and 2) when the rate of information flow is below a threshold, running consensus achieves only a fraction of the Chernoff information rate. We quantify this achievable rate as a function of the network rate of information flow. Simulation examples demonstrate our theoretical findings on the behavior of running consensus based detection over random networks.

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Section: Introductionmentioning

confidence: 99%

Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis

Bajović

Jakovetić

Xavier

et al. 2011

IEEE Trans. Signal Process.

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“…Therefore, distributed signal processing algorithms, which take these limitations into consideration, are needed in such cases. Many distributed algorithms have been proposed in recent years [10], such as the distributed parameter estimation [11][12][13][14][15][16][17][18][19][20], distributed Kalman filtering [21,22], distributed detection [23,24], distributed clustering [25,26], and distributed information-theoretic learning [27,28]. In a majority of these distributed algorithms, signal processing tasks are accomplished at each node based on local computation, local data, as well as limited information exchange among neighbor nodes.…”

Section: Introductionmentioning

confidence: 99%

Distributed Vector Quantization Based on Kullback-Leibler Divergence

Shen

Luo

2015

Entropy

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Abstract:The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data. Distributed signal processing has received much attention in recent years, since in many applications data are dispersedly collected/stored in distributed nodes over networks, but centralizing all these data to one processing center is sometimes impractical. In this paper, we develop a distributed vector quantization (VQ) algorithm based on Kullback-Leibler (K-L) divergence. We start from the centralized case and propose to minimize the K-L divergence between the distribution of global original data and the distribution of global reproduction vectors, and then obtain an online iterative solution to this optimization problem based on the Robbins-Monro stochastic approximation. Afterwards, we extend the solution to apply to distributed cases by introducing diffusion cooperation among nodes. Numerical simulations show that the performances of the distributed K-L-based VQ algorithm are very close to the corresponding centralized algorithm. Besides, both the centralized and distributed K-L-based VQ show more robustness to outliers than the (centralized) Linde-Buzo-Gray (LBG) algorithm and the (centralized) self-organization map (SOM) algorithm.

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“…It is likely that future data acquisition, control, and physical monitoring, will heavily rely on this type of networks. Distributed estimation in a linear least squares (LLS) framework has been widely studied in the sensor network literature (see, e.g., [2]- [10]). The LLS framework is applied for linear regression problems, and provides a solution for an overdetermined system of linear equations, i.e., with an data matrix and an -dimensional data vector with .…”

Section: Introductionmentioning

confidence: 99%

“…The aim is then to find a vector that minimizes the squared error between the left-and the right-hand side (LHS) (RHS), i.e., (1) where denotes the Euclidean norm ( -norm). In a WSN, nodes can either have access to subsets of the columns of [2]- [4], e.g., for distributed signal enhancement and beamforming [11], or to subsets of the rows of (and ) [5]- [10], e.g., for distributed system identification. Despite this common cost function, both problems are tackled in very different ways.…”

Section: Introductionmentioning

confidence: 99%

“…Distributed LLS estimation amounts to computing the network-wide LLS solution in a distributed way, where the nodes have to reach a consensus on a common network-wide parameter vector . A motivation for this type of distributed estimation problems, different types of algorithms, and possible applications, can be found in [5]- [10] and references therein.…”

Section: Introductionmentioning

confidence: 99%

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Consensus-Based Distributed Total Least Squares Estimation in Ad Hoc Wireless Sensor Networks

Bertrand

Moonen

2011

IEEE Trans. Signal Process.

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Abstract-Total least squares (TLS) is a popular solution technique for overdetermined systems of linear equations, where both the right-hand side and the input data matrix are assumed to be noisy. We consider a TLS problem in an ad hoc wireless sensor network, where each node collects observations that yield a node-specific subset of linear equations. The goal is to compute the TLS solution of the full set of equations in a distributed fashion, without gathering all these equations in a fusion center. To facilitate the use of the dual-based subgradient algorithm (DBSA), we transform the TLS problem to an equivalent convex semidefinite program (SDP), based on semidefinite relaxation (SDR). This allows us to derive a distributed TLS (D-TLS) algorithm, that satisfies the conditions for convergence of the DBSA, and obtains the same solution as the original (unrelaxed) TLS problem. Even though we make a detour through SDR and SDP theory, the resulting D-TLS algorithm relies on solving local TLS-like problems at each node, rather than computationally expensive SDP optimization techniques. The algorithm is flexible and fully distributed, i.e., it does not make any assumptions on the network topology and nodes only share data with their neighbors through local broadcasts. Due to the flexibility and the uniformity of the network, there is no single point of failure, which makes the algorithm robust to sensor failures. Monte Carlo simulation results are provided to demonstrate the effectiveness of the method.

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Distributed Recursive Least-Squares for Consensus-Based In-Network Adaptive Estimation

Cited by 131 publications

References 7 publications

Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis

Distributed Detection via Gaussian Running Consensus: Large Deviations Asymptotic Analysis

Distributed Vector Quantization Based on Kullback-Leibler Divergence

Consensus-Based Distributed Total Least Squares Estimation in Ad Hoc Wireless Sensor Networks

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