9,965 research outputs found
Robust Localization from Incomplete Local Information
We consider the problem of localizing wireless devices in an ad-hoc network
embedded in a d-dimensional Euclidean space. Obtaining a good estimation of
where wireless devices are located is crucial in wireless network applications
including environment monitoring, geographic routing and topology control. When
the positions of the devices are unknown and only local distance information is
given, we need to infer the positions from these local distance measurements.
This problem is particularly challenging when we only have access to
measurements that have limited accuracy and are incomplete. We consider the
extreme case of this limitation on the available information, namely only the
connectivity information is available, i.e., we only know whether a pair of
nodes is within a fixed detection range of each other or not, and no
information is known about how far apart they are. Further, to account for
detection failures, we assume that even if a pair of devices is within the
detection range, it fails to detect the presence of one another with some
probability and this probability of failure depends on how far apart those
devices are. Given this limited information, we investigate the performance of
a centralized positioning algorithm MDS-MAP introduced by Shang et al., and a
distributed positioning algorithm, introduced by Savarese et al., called
HOP-TERRAIN. In particular, for a network consisting of n devices positioned
randomly, we provide a bound on the resulting error for both algorithms. We
show that the error is bounded, decreasing at a rate that is proportional to
R/Rc, where Rc is the critical detection range when the resulting random
network starts to be connected, and R is the detection range of each device.Comment: 40 pages, 13 figure
Bayesian CRLB for hybrid ToA and DoA based wireless localization with anchor uncertainty
In this paper, we derive the Bayesian Cramér-Rao lower bound for three dimensional hybrid localization using time-of-arrival (ToA) and direction-of-arrival (DoA) types of measurements. Unlike previous works, we include the practical constraint that the anchor position is not known exactly but rather up to some error. The resulting bound can be used for error analysis of such a localization system or as an optimality
criterion for the selection of suitable anchors
Effects of Spatial Randomness on Locating a Point Source with Distributed Sensors
Most studies that consider the problem of estimating the location of a point
source in wireless sensor networks assume that the source location is estimated
by a set of spatially distributed sensors, whose locations are fixed. Motivated
by the fact that the observation quality and performance of the localization
algorithm depend on the location of the sensors, which could be randomly
distributed, this paper investigates the performance of a recently proposed
energy-based source-localization algorithm under the assumption that the
sensors are positioned according to a uniform clustering process. Practical
considerations such as the existence and size of the exclusion zones around
each sensor and the source will be studied. By introducing a novel performance
measure called the estimation outage, it will be shown how parameters related
to the network geometry such as the distance between the source and the closest
sensor to it as well as the number of sensors within a region surrounding the
source affect the localization performance.Comment: 7 Pages, 5 Figures, To appear at the 2014 IEEE International
Conference on Communications (ICC'14) Workshop on Advances in Network
Localization and Navigation (ANLN), Invited Pape
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