50 research outputs found
On Sequential Estimation of Linear Models From Data with Correlated Noise
Publication in the conference proceedings of EUSIPCO, Lisbon, Portugal, 201
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
Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach
Nowadays there is a growing research interest on the possibility of enriching
small flying robots with autonomous sensing and online navigation capabilities.
This will enable a large number of applications spanning from remote
surveillance to logistics, smarter cities and emergency aid in hazardous
environments. In this context, an emerging problem is to track unauthorized
small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in
large UAV networks. In contrast with current solutions mainly based on static
and on-ground radars, this paper proposes the idea of a dynamic radar network
of UAVs for real-time and high-accuracy tracking of malicious targets. To this
end, we describe a solution for real-time navigation of UAVs to track a dynamic
target using heterogeneously sensed information. Such information is shared by
the UAVs with their neighbors via multi-hops, allowing tracking the target by a
local Bayesian estimator running at each agent. Since not all the paths are
equal in terms of information gathering point-of-view, the UAVs plan their own
trajectory by minimizing the posterior covariance matrix of the target state
under UAV kinematic and anti-collision constraints. Our results show how a
dynamic network of radars attains better localization results compared to a
fixed configuration and how the on-board sensor technology impacts the accuracy
in tracking a target with different radar cross sections, especially in non
line-of-sight (NLOS) situations
Unsupervised State-Space Modeling Using Reproducing Kernels
This is the accepted manuscript. The final version is available at http://dx.doi.org/10.1109/TSP.2015.2448527.A novel framework for the design of state-space models (SSMs) is proposed whereby the state-transition function of the model is parametrised using reproducing kernels. The
nature of SSMs requires learning a latent function that resides
in the state space and for which input-output sample pairs are not
available, thus prohibiting the use of gradient-based supervised
kernel learning. To this end, we then propose to learn the mixing
weights of the kernel estimate by sampling from their posterior
density using Monte Carlo methods. We first introduce an offline
version of the proposed algorithm, followed by an online version
which performs inference on both the parameters and the hidden
state through particle filtering. The accuracy of the estimation
of the state-transition function is first validated on synthetic
data. Next, we show that the proposed algorithm outperforms
kernel adaptive filters in the prediction of real-world time series,
while also providing probabilistic estimates, a key advantage over
standard methods.Felipe Tobar acknowledges financial support from EPSRC grant number EP/L000776/1
Collaborative Target-Localization and Information-Based Control in Networks of UAVs
In this paper, we study the capacity of UAV networks for high-accuracy localization of targets. We address the problem of designing a distributed control scheme for UAV navigation and formation based on an information-seeking criterion maximizing the target localization accuracy. Each UAV is assumed to be able to communicate and collaborate with other UAVs that are within a neighboring region, allowing for a feasible distributed solution which takes into account a trade-off between localization accuracy and speed of convergence to a suitable localization of the target. Such an investigation also considers communication latency constraints as well as safety requirements such as inter-UAV and obstacle collision avoidance
Adapting the number of particles in sequential Monte Carlo methods through an online scheme for convergence assessment
Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the filter is guaranteed when the number of particles tends to infinity, the quality of the approximation is usually unknown but strongly dependent on the number of particles. In this paper, we propose a novel method for assessing the convergence of particle filters in an online manner, as well as a simple scheme for the online adaptation of the number of particles based on the convergence assessment. The method is based on a sequential comparison between the actual observations and their predictive probability distributions approximated by the filter. We provide a rigorous theoretical analysis of the proposed methodology and, as an example of its practical use, we present simulations of a simple algorithm for the dynamic and online adaptation of the number of particles during the operation of a particle filter on a stochastic version of the Lorenz 63 system.This work was supported in part by the Ministerio de EconomĂa y Competitividad of Spain under Grant TEC2013-41718-R OTOSiS, Grant TEC2012-38883-C02-01 COMPREHENSION, and Grant TEC2015-69868-C2-1-R ADVENTURE, in part by the Office of Naval Research Global under Grant N62909-15-1-2011, and in part by the National Science Foundation under Grant CCF-1320626 and Grant CCF-1618999