279 research outputs found
Likelihood Consensus and Its Application to Distributed Particle Filtering
We consider distributed state estimation in a wireless sensor network without
a fusion center. Each sensor performs a global estimation task---based on the
past and current measurements of all sensors---using only local processing and
local communications with its neighbors. In this estimation task, the joint
(all-sensors) likelihood function (JLF) plays a central role as it epitomizes
the measurements of all sensors. We propose a distributed method for computing,
at each sensor, an approximation of the JLF by means of consensus algorithms.
This "likelihood consensus" method is applicable if the local likelihood
functions of the various sensors (viewed as conditional probability density
functions of the local measurements) belong to the exponential family of
distributions. We then use the likelihood consensus method to implement a
distributed particle filter and a distributed Gaussian particle filter. Each
sensor runs a local particle filter, or a local Gaussian particle filter, that
computes a global state estimate. The weight update in each local (Gaussian)
particle filter employs the JLF, which is obtained through the likelihood
consensus scheme. For the distributed Gaussian particle filter, the number of
particles can be significantly reduced by means of an additional consensus
scheme. Simulation results are presented to assess the performance of the
proposed distributed particle filters for a multiple target tracking problem
Self-Supervised and Invariant Representations for Wireless Localization
In this work, we present a wireless localization method that operates on
self-supervised and unlabeled channel estimates. Our self-supervising method
learns general-purpose channel features robust to fading and system
impairments. Learned representations are easily transferable to new
environments and ready to use for other wireless downstream tasks. To the best
of our knowledge, the proposed method is the first joint-embedding
self-supervised approach to forsake the dependency on contrastive channel
estimates. Our approach outperforms fully-supervised techniques in small data
regimes under fine-tuning and, in some cases, linear evaluation. We assess the
performance in centralized and distributed massive MIMO systems for multiple
datasets. Moreover, our method works indoors and outdoors without additional
assumptions or design changes
- …