113 research outputs found
Outlier-Detection Based Robust Information Fusion for Networked Systems
We consider state estimation for networked systems where measurements from
sensor nodes are contaminated by outliers. A new hierarchical measurement model
is formulated for outlier detection by integrating the outlier-free measurement
model with a binary indicator variable. The binary indicator variable, which is
assigned a beta-Bernoulli prior, is utilized to characterize if the sensor's
measurement is nominal or an outlier. Based on the proposed outlier-detection
measurement model, both centralized and decentralized information fusion
filters are developed. Specifically, in the centralized approach, all
measurements are sent to a fusion center where the state and outlier indicators
are jointly estimated by employing the mean-field variational Bayesian
inference in an iterative manner. In the decentralized approach, however, every
node shares its information, including the prior and likelihood, only with its
neighbors based on a hybrid consensus strategy. Then each node independently
performs the estimation task based on its own and shared information. In
addition, an approximation distributed solution is proposed to reduce the local
computational complexity and communication overhead. Simulation results reveal
that the proposed algorithms are effective in dealing with outliers compared
with several recent robust solutions
Distributed Variational Inference for Online Supervised Learning
Developing efficient solutions for inference problems in intelligent sensor
networks is crucial for the next generation of location, tracking, and mapping
services. This paper develops a scalable distributed probabilistic inference
algorithm that applies to continuous variables, intractable posteriors and
large-scale real-time data in sensor networks. In a centralized setting,
variational inference is a fundamental technique for performing approximate
Bayesian estimation, in which an intractable posterior density is approximated
with a parametric density. Our key contribution lies in the derivation of a
separable lower bound on the centralized estimation objective, which enables
distributed variational inference with one-hop communication in a sensor
network. Our distributed evidence lower bound (DELBO) consists of a weighted
sum of observation likelihood and divergence to prior densities, and its gap to
the measurement evidence is due to consensus and modeling errors. To solve
binary classification and regression problems while handling streaming data, we
design an online distributed algorithm that maximizes DELBO, and specialize it
to Gaussian variational densities with non-linear likelihoods. The resulting
distributed Gaussian variational inference (DGVI) efficiently inverts a
-rank correction to the covariance matrix. Finally, we derive a diagonalized
version for online distributed inference in high-dimensional models, and apply
it to multi-robot probabilistic mapping using indoor LiDAR data
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
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