495 research outputs found
Cooperative Synchronization in Wireless Networks
Synchronization is a key functionality in wireless network, enabling a wide
variety of services. We consider a Bayesian inference framework whereby network
nodes can achieve phase and skew synchronization in a fully distributed way. In
particular, under the assumption of Gaussian measurement noise, we derive two
message passing methods (belief propagation and mean field), analyze their
convergence behavior, and perform a qualitative and quantitative comparison
with a number of competing algorithms. We also show that both methods can be
applied in networks with and without master nodes. Our performance results are
complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds
Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks
Cooperative localization in agent networks based on interagent time-of-flight
measurements is closely related to synchronization. To leverage this relation,
we propose a Bayesian factor graph framework for cooperative simultaneous
localization and synchronization (CoSLAS). This framework is suited to mobile
agents and time-varying local clock parameters. Building on the CoSLAS factor
graph, we develop a distributed (decentralized) belief propagation algorithm
for CoSLAS in the practically important case of an affine clock model and
asymmetric time stamping. Our algorithm allows for real-time operation and is
suitable for a time-varying network connectivity. To achieve high accuracy at
reduced complexity and communication cost, the algorithm combines particle
implementations with parametric message representations and takes advantage of
a conditional independence property. Simulation results demonstrate the good
performance of the proposed algorithm in a challenging scenario with
time-varying network connectivity.Comment: 13 pages, 6 figures, 3 tables; manuscript submitted to IEEE
Transaction on Signal Processin
TOA-based passive localization constructed over factor graphs: A unified framework
© 2019 IEEE. Passive localization based on time of arrival (TOA) measurements is investigated, where the transmitted signal is reflected by a passive target and then received at several distributed receivers. After collecting all measurements at receivers, we can determine the target location. The aim of this paper is to provide a unified factor graph-based framework for passive localization in wireless sensor networks based on TOA measurements. Relying on the linearization of range measurements, we construct a Forney-style factor graph model and conceive the corresponding Gaussian message passing algorithm to obtain the target location. It is shown that the factor graph can be readily modified for handling challenging scenarios such as uncertain receiver positions and link failures. Moreover, a distributed localization method based on consensus-aided operation is proposed for a large-scale resource constrained network operating without a fusion center. Furthermore, we derive the Cramér-Rao bound (CRB) to evaluate the performance of the proposed algorithm. Our simulation results verify the efficiency of the proposed unified approach and of its distributed implementation
Distributed Estimation with Information-Seeking Control in Agent Network
We introduce a distributed, cooperative framework and method for Bayesian
estimation and control in decentralized agent networks. Our framework combines
joint estimation of time-varying global and local states with
information-seeking control optimizing the behavior of the agents. It is suited
to nonlinear and non-Gaussian problems and, in particular, to location-aware
networks. For cooperative estimation, a combination of belief propagation
message passing and consensus is used. For cooperative control, the negative
posterior joint entropy of all states is maximized via a gradient ascent. The
estimation layer provides the control layer with probabilistic information in
the form of sample representations of probability distributions. Simulation
results demonstrate intelligent behavior of the agents and excellent estimation
performance for a simultaneous self-localization and target tracking problem.
In a cooperative localization scenario with only one anchor, mobile agents can
localize themselves after a short time with an accuracy that is higher than the
accuracy of the performed distance measurements.Comment: 17 pages, 10 figure
Probabilistic Graphical Models: an Application in Synchronization and Localization
Die Lokalisierung von mobilen Nutzern (MU) in sehr dichten Netzen erfordert häufig die Synchronisierung der Access Points (APs) untereinander. Erstens konzentriert sich diese Arbeit auf die Lösung des Problems der Zeitsynchronisation in 5G-Netzwerken, indem ein hybrider Bayesischer Ansatz für die Schätzung des Taktversatzes und des Versatzes verwendet wird. Wir untersuchen und demonstrieren den beträchtlichen Nutzen der Belief Propagation (BP), die auf factor graphs läuft, um eine präzise netzwerkweite Synchronisation zu erreichen. Darüber hinaus nutzen wir die Vorteile der Bayesischen Rekursiven Filterung (BRF), um den Zeitstempel-Fehler bei der paarweisen Synchronisierung zu verringern. Schließlich zeigen wir die Vorzüge der hybriden Synchronisation auf, indem wir ein großes Netzwerk in gemeinsame und lokale Synchronisationsdomänen unterteilen und so den am besten geeigneten Synchronisationsalgorithmus (BP- oder BRF-basiert) auf jede Domäne anwenden können.
Zweitens schlagen wir einen Deep Neural Network (DNN)-gestützten Particle Filter-basierten (DePF)-Ansatz vor, um das gemeinsame MU-Sync&loc-Problem zu lösen. Insbesondere setzt DePF einen asymmetrischen Zeitstempel-Austauschmechanismus zwischen den MUs und den APs ein, der Informationen über den Taktversatz, die Zeitverschiebung der MUs, und die AP-MU Abstand liefert. Zur Schätzung des Ankunftswinkels des empfangenen Synchronisierungspakets nutzt DePF den multiple signal classification Algorithmus, der durch die Channel Impulse Response (CIR) der Synchronisierungspakete gespeist wird. Die CIR wird auch genutzt, um den Verbindungszustand zu bestimmen, d. h. Line-of-Sight (LoS) oder Non-LoS (NLoS). Schließlich nutzt DePF particle Gaussian mixtures, die eine hybride partikelbasierte und parametrische BRF-Fusion der vorgenannten Informationen ermöglichen und die Position und die Taktparameter der MUs gemeinsam schätzen.Mobile User (MU) localization in ultra dense networks often requires, on one hand, the Access Points (APs) to be synchronized among each other, and, on the other hand, the MU-AP synchronization. In this work, we firstly address the former, which eventually provides a basis for the latter, i.e., for the joint MU synchronization and localization (sync&loc). In particular, firstly, this work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Specifically, we investigate and demonstrate the substantial benefit of Belief Propagation (BP) running on Factor Graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian Recursive Filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into common and local synchronization domains, thereby being able to apply the most suitable synchronization algorithm (BP- or BRF-based) on each domain.
Secondly, we propose a Deep Neural Network (DNN)-assisted Particle Filter-based (DePF) approach to address the MU joint sync&loc problem. In particular, DePF deploys an asymmetric time-stamp exchange mechanism between the MUs and the APs, which provides information about the MUs' clock offset, skew, and AP-MU distance. In addition, to estimate the Angle of Arrival (AoA) of the received synchronization packet, DePF draws on the Multiple Signal Classification (MUSIC) algorithm that is fed by the Channel Impulse Response (CIR) experienced by the sync packets. The CIR is also leveraged on to determine the link condition, i.e. Line-of-Sight (LoS) or Non-LoS (NLoS). Finally DePF capitalizes on particle Gaussian mixtures which allow for a hybrid particle-based and parametric BRF fusion of the aforementioned pieces of information and jointly estimate the position and clock parameters of the MUs
Secure Localization and Velocity Estimation in Mobile IoT Networks with Malicious Attacks
IEEE Secure localization and velocity estimation are of great importance in Internet of Things (IoT) applications and are particularly challenging in the presence of malicious attacks. The problem becomes even more challenging in practical scenarios in which attack information is unknown and anchor node location uncertainties occur due to node mobility and falsification of malicious nodes. This challenging problem is investigated in this paper. With reasonable assumptions on the attack model and uncertainties, the secure localization and velocity estimation problem is formulated as an intractable maximum a posterior (MAP) problem. A variational-message-passing (VMP) based algorithm is proposed to approximate the true posterior distribution iteratively and find the closed-form estimates of the location and velocity securely. The identification of malicious nodes is also achieved in the meantime. The convergence of the proposed VMP-based algorithm is also discussed. Numerical simulations are finally conducted and the results show the VMP-based joint localization and velocity estimation algorithm can approach the Bayesian Cramer Rao bound and is superior to other secure algorithms
Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties
Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors’ positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target’s location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithm
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