12,948 research outputs found
Nested Distributed Gradient Methods with Adaptive Quantized Communication
In this paper, we consider minimizing a sum of local convex objective
functions in a distributed setting, where communication can be costly. We
propose and analyze a class of nested distributed gradient methods with
adaptive quantized communication (NEAR-DGD+Q). We show the effect of performing
multiple quantized communication steps on the rate of convergence and on the
size of the neighborhood of convergence, and prove R-Linear convergence to the
exact solution with increasing number of consensus steps and adaptive
quantization. We test the performance of the method, as well as some practical
variants, on quadratic functions, and show the effects of multiple quantized
communication steps in terms of iterations/gradient evaluations, communication
and cost.Comment: 9 pages, 2 figures. arXiv admin note: text overlap with
arXiv:1709.0299
Robust Distributed Fusion with Labeled Random Finite Sets
This paper considers the problem of the distributed fusion of multi-object
posteriors in the labeled random finite set filtering framework, using
Generalized Covariance Intersection (GCI) method. Our analysis shows that GCI
fusion with labeled multi-object densities strongly relies on label
consistencies between local multi-object posteriors at different sensor nodes,
and hence suffers from a severe performance degradation when perfect label
consistencies are violated. Moreover, we mathematically analyze this phenomenon
from the perspective of Principle of Minimum Discrimination Information and the
so called yes-object probability. Inspired by the analysis, we propose a novel
and general solution for the distributed fusion with labeled multi-object
densities that is robust to label inconsistencies between sensors.
Specifically, the labeled multi-object posteriors are firstly marginalized to
their unlabeled posteriors which are then fused using GCI method. We also
introduce a principled method to construct the labeled fused density and
produce tracks formally. Based on the developed theoretical framework, we
present tractable algorithms for the family of generalized labeled
multi-Bernoulli (GLMB) filters including -GLMB, marginalized
-GLMB and labeled multi-Bernoulli filters. The robustness and
efficiency of the proposed distributed fusion algorithm are demonstrated in
challenging tracking scenarios via numerical experiments.Comment: 17pages, 23 figure
Distributed tracking control of leader-follower multi-agent systems under noisy measurement
In this paper, a distributed tracking control scheme with distributed
estimators has been developed for a leader-follower multi-agent system with
measurement noises and directed interconnection topology. It is supposed that
each follower can only measure relative positions of its neighbors in a noisy
environment, including the relative position of the second-order active leader.
A neighbor-based tracking protocol together with distributed estimators is
designed based on a novel velocity decomposition technique. It is shown that
the closed loop tracking control system is stochastically stable in mean square
and the estimation errors converge to zero in mean square as well. A simulation
example is finally given to illustrate the performance of the proposed control
scheme.Comment: 8 Pages, 3 figure
MAX-consensus in open multi-agent systems with gossip interactions
We study the problem of distributed maximum computation in an open
multi-agent system, where agents can leave and arrive during the execution of
the algorithm. The main challenge comes from the possibility that the agent
holding the largest value leaves the system, which changes the value to be
computed. The algorithms must as a result be endowed with mechanisms allowing
to forget outdated information. The focus is on systems in which interactions
are pairwise gossips between randomly selected agents. We consider situations
where leaving agents can send a last message, and situations where they cannot.
For both cases, we provide algorithms able to eventually compute the maximum of
the values held by agents.Comment: To appear in the proceedings of the 56th IEEE Conference on Decision
and Control (CDC 17). 8 pages, 3 figure
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