348 research outputs found
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
Multisensor Poisson Multi-Bernoulli Filter for Joint Target-Sensor State Tracking
In a typical multitarget tracking (MTT) scenario, the sensor state is either
assumed known, or tracking is performed in the sensor's (relative) coordinate
frame. This assumption does not hold when the sensor, e.g., an automotive
radar, is mounted on a vehicle, and the target state should be represented in a
global (absolute) coordinate frame. Then it is important to consider the
uncertain location of the vehicle on which the sensor is mounted for MTT. In
this paper, we present a multisensor low complexity Poisson multi-Bernoulli MTT
filter, which jointly tracks the uncertain vehicle state and target states.
Measurements collected by different sensors mounted on multiple vehicles with
varying location uncertainty are incorporated sequentially based on the arrival
of new sensor measurements. In doing so, targets observed from a sensor mounted
on a well-localized vehicle reduce the state uncertainty of other poorly
localized vehicles, provided that a common non-empty subset of targets is
observed. A low complexity filter is obtained by approximations of the joint
sensor-feature state density minimizing the Kullback-Leibler divergence (KLD).
Results from synthetic as well as experimental measurement data, collected in a
vehicle driving scenario, demonstrate the performance benefits of joint
vehicle-target state tracking.Comment: 13 pages, 7 figure
A survey on wireless indoor localization from the device perspective
With the marvelous development of wireless techniques and ubiquitous deployment of wireless systems indoors, myriad indoor location-based services (ILBSs) have permeated into numerous aspects of modern life. The most fundamental functionality is to pinpoint the location of the target via wireless devices. According to how wireless devices interact with the target, wireless indoor localization schemes roughly fall into two categories: device based and device free. In device-based localization, a wireless device (e.g., a smartphone) is attached to the target and computes its location through cooperation with other deployed wireless devices. In device-free localization, the target carries no wireless devices, while the wireless infrastructure deployed in the environment determines the target’s location by analyzing its impact on wireless signals.
This article is intended to offer a comprehensive state-of-the-art survey on wireless indoor localization from the device perspective. In this survey, we review the recent advances in both modes by elaborating on the underlying wireless modalities, basic localization principles, and data fusion techniques, with special emphasis on emerging trends in (1) leveraging smartphones to integrate wireless and sensor capabilities and extend to the social context for device-based localization, and (2) extracting specific wireless features to trigger novel human-centric device-free localization. We comprehensively compare each scheme in terms of accuracy, cost, scalability, and energy efficiency. Furthermore, we take a first look at intrinsic technical challenges in both categories and identify several open research issues associated with these new challenges.</jats:p
A Statistically Modelling Method for Performance Limits in Sensor Localization
In this paper, we study performance limits of sensor localization from a
novel perspective. Specifically, we consider the Cramer-Rao Lower Bound (CRLB)
in single-hop sensor localization using measurements from received signal
strength (RSS), time of arrival (TOA) and bearing, respectively, but
differently from the existing work, we statistically analyze the trace of the
associated CRLB matrix (i.e. as a scalar metric for performance limits of
sensor localization) by assuming anchor locations are random. By the Central
Limit Theorems for -statistics, we show that as the number of the anchors
increases, this scalar metric is asymptotically normal in the RSS/bearing case,
and converges to a random variable which is an affine transformation of a
chi-square random variable of degree 2 in the TOA case. Moreover, we provide
formulas quantitatively describing the relationship among the mean and standard
deviation of the scalar metric, the number of the anchors, the parameters of
communication channels, the noise statistics in measurements and the spatial
distribution of the anchors. These formulas, though asymptotic in the number of
the anchors, in many cases turn out to be remarkably accurate in predicting
performance limits, even if the number is small. Simulations are carried out to
confirm our results
Cooperative Localization of Vehicles without Inter-vehicle Measurements
While cooperation among vehicles can improve localization, standard communication technologies (e.g., 802.11p) cannot provide reliable range or angle measurements. To allow cooperation without explicit inter-vehicle measurements, we propose a cooperative localization method whereby vehicles track mobile features in the environment and use associations of features among vehicles to improve the vehicles\u27 localization accuracy. The proposed algorithm, which scales linearly in the number of vehicles and quadratically in the number of tracked features, shows superior localization performance compared to a non-cooperative approach
Cooperative Localization for Mobile Networks:A Distributed Belief Propagation – Mean Field Message Passing Algorithm
We propose a hybrid message passing method for distributed cooperative
localization and tracking of mobile agents. Belief propagation and mean field
message passing are employed for, respectively, the motion-related and
measurement-related part of the factor graph. Using a Gaussian belief
approximation, only three real values per message passing iteration have to be
broadcast to neighboring agents. Despite these very low communication
requirements, the estimation accuracy can be comparable to that of
particle-based belief propagation.Comment: 5 pages, 1 figur
Location Awareness in Beyond 5G Networks
Location awareness is essential for enabling contextual
services and for improving network management in 5th
generation (5G) and beyond 5G (B5G) networks. This paper
provides an overview of the expanding opportunities offered
by location awareness in wireless networks, discusses soft information
(SI)-based approaches for improved location awareness,
and presents case studies in conformity to the 3rd Generation
Partnership Project (3GPP) standardization by the European
Telecommunications Standards Institute (ETSI). Results show
that SI-based approaches can provide a new level of location
awareness in 5G and B5G networks
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