A scheme for robust distributed sensor fusion based on average consensus

By Lin Xiao, Stephen P. Boyd, Sanjay Lall. 2005

1,500 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Decentralized Deep Learning with Arbitrary Communication Compression (2019, background): “It has been observed that gossip averaging can diverge (or not converge to the correct solution) in the presence of quantization noise (Xiao et al., 2005; Carli et al., 2007; Nedić et al., 2008; Dimakis et al., 2010; Carli et al., 2010b; Yuan et al., 2012).”
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication (2019, background): “However, for consensus algorithms with compressed communication it has been remarked that the standard gossip algorithm does not converge to the correct solution (Xiao et al., 2005).”
Gossip consensus algorithms via quantized communication (2009, background): “Related works The constraint of quantization, due to the use of digital channels or to computing and memory constraints, has been considered in consensus problems in several recent papers [17,4,8,9,1,7,13,18].”
Distributed Control Strategies for Microgrids: An Overview (2020, neutral): “Furthermore, techniques have been developed based on the adaptive adjustment of the adjacency matrix to improve the convergence and stability of the graph [77].”
Information consensus in multivehicle cooperative control (2007, neutral): “Consensus algorithms have applications in rendezvous [2]–[4], formation control [5]–[9], flocking [10]–[16], attitude alignment [17]–[19], and sensor networks [20]–[23].”
Quantized average consensus via dynamic coding/decoding schemes (2008, methodology): “The matrix P is built using the Metropolis weights [11].”
A new perspective on randomized gossip algorithms (2016, background): “The average consensus problem and randomized gossip algorithms for solving it appear in many applications, including distributed data fusion in sensor networks [1], load balancing [2] and clock synchronization [3].”
H ∞ consensus control of multi-agent systems with switching topology: a dynamic output feedback protocol (2010, background): “…the consensus problem of such a system has attracted the attention of some researchers (Ögren, Fiorelli, and Leonard 2004; Olfati-Saber 2005; Ren, Beard, and Kingston 2005; Xiao, Boyd, and Lall 2005; Xiao, Boyd, and Kim 2007; Li and Zhang 2008; Lin, Jia, and Li 2008b; Li, Duan, and Huang 2009).”
Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability (2014, methodology): “(16) Recalling the previous definition of consensus matrix Π , the following proposition derives fromwell-known results on distributed averaging (Olfati-Saber et al., 2007; Xiao et al., 2005).”
Distributed Consensus With Limited Communication Data Rate (2011, background): “Average-consensus protocols can be viewed as distributed least-mean-square estimation algorithms in sensor networks ([36]).”
On the Convergence Time of Asynchronous Distributed Quantized Averaging Algorithms (2010, background): “ problem requires that the consensus value be the average of individual initial states. The distributed averaging algorithm acts as the building block for many distributed tasks such as sensor fusion [16] and distributed estimation [12]. In real-world communication networks, the capacities of commun”
Design and analysis of distributed averaging with quantized communication (2014, background): “al of obtaining algorithms with improved convergence rates. One particular choice, which deﬁnes what has come to be known as the Metropolis algorithm, requires only local information to deﬁne the wij [40,41]. The corresponding Metropolis weights are chosen as follows: wij = 1 max{di,dj}+1 , ∀(i,j) ∈”
Distributed average consensus with least-mean-square deviation (2007, background): “, [13,20,26,34]) and with asynchronous communication and computation ([2]; see also the early work in [31,32]).”
Experimental Validation of Consensus Algorithms for Multivehicle Cooperative Control (2008, methodology): “Consensus algorithms have applications in rendezvous [17]–[20], formation control [21]–[23], flocking [24]–[26], attitude alignment [27], [28], decentralized task assignment [29], sensor fusion [30]–[32], etc.”
Consensus-Based Linear and Nonlinear Filtering (2015, background): “For what concerns parameter estimation, consensus-based techniques with guaranteed performance can be found for example in [2], [3].”