8,009 research outputs found
Lower bounds for Arrangement-based Range-Free Localization in Sensor Networks
Colander are location aware entities that collaborate to determine
approximate location of mobile or static objects when beacons from an object
are received by all colanders that are within its distance . This model,
referred to as arrangement-based localization, does not require distance
estimation between entities, which has been shown to be highly erroneous in
practice. Colander are applicable in localization in sensor networks and
tracking of mobile objects.
A set is an -colander if by placing
receivers at the points of , a wireless device with transmission radius
can be localized to within a circle of radius . We present tight
upper and lower bounds on the size of -colanders. We measure the
expected size of colanders that will form -colanders if they
distributed uniformly over the plane
On the structural nature of cooperation in distributed network localization
We demonstrate analytically that the contribution of cooperation in improving the accuracy of distributed network localization has a fundamentally structural nature, rather then statistical as widely believed. To this end we first introduce a new approach to build Fisher Information Matrices (FIMs), in which the individual contribution of each cooperative pair of nodes is captured explicitly by a corresponding information vector. The approach offers new insight onto the structure of FIMs, enabling us to easily account for both anchor and node location uncertainties in assessing lower bounds on localization errors. Using this construction it is surprisingly found that in the presence of node location uncertainty and regardless of ranging error variances or network size, the Fisher information matrix (FIM) terms corresponding to the information added by node-to-node cooperation nearly vanish. In other words, the analysis reveals that the key contribution of cooperation in network localization is not to add statistical node-to-node information (in the Fisher sense), but rather to provide a structure over which information is better exploited
Power Optimization for Network Localization
Reliable and accurate localization of mobile objects is essential for many
applications in wireless networks. In range-based localization, the position of
the object can be inferred using the distance measurements from wireless
signals exchanged with active objects or reflected by passive ones. Power
allocation for ranging signals is important since it affects not only network
lifetime and throughput but also localization accuracy. In this paper, we
establish a unifying optimization framework for power allocation in both active
and passive localization networks. In particular, we first determine the
functional properties of the localization accuracy metric, which enable us to
transform the power allocation problems into second-order cone programs
(SOCPs). We then propose the robust counterparts of the problems in the
presence of parameter uncertainty and develop asymptotically optimal and
efficient near-optimal SOCP-based algorithms. Our simulation results validate
the efficiency and robustness of the proposed algorithms.Comment: 15 pages, 7 figure
Non-line-of-sight Node Localization based on Semi-Definite Programming in Wireless Sensor Networks
An unknown-position sensor can be localized if there are three or more
anchors making time-of-arrival (TOA) measurements of a signal from it. However,
the location errors can be very large due to the fact that some of the
measurements are from non-line-of-sight (NLOS) paths. In this paper, we propose
a semi-definite programming (SDP) based node localization algorithm in NLOS
environment for ultra-wideband (UWB) wireless sensor networks. The positions of
sensors can be estimated using the distance estimates from location-aware
anchors as well as other sensors. However, in the absence of LOS paths, e.g.,
in indoor networks, the NLOS range estimates can be significantly biased. As a
result, the NLOS error can remarkably decrease the location accuracy.
And it is not easy to efficiently distinguish LOS from NLOS measurements. In
this paper, an algorithm is proposed that achieves high location accuracy
without the need of identifying NLOS and LOS measurement.Comment: submitted to IEEE ICC'1
Low cost underwater acoustic localization
Over the course of the last decade, the cost of marine robotic platforms has
significantly decreased. In part this has lowered the barriers to entry of
exploring and monitoring larger areas of the earth's oceans. However, these
advances have been mostly focused on autonomous surface vehicles (ASVs) or
shallow water autonomous underwater vehicles (AUVs). One of the main drivers
for high cost in the deep water domain is the challenge of localizing such
vehicles using acoustics. A low cost one-way travel time underwater ranging
system is proposed to assist in localizing deep water submersibles. The system
consists of location aware anchor buoys at the surface and underwater nodes.
This paper presents a comparison of methods together with details on the
physical implementation to allow its integration into a deep sea micro AUV
currently in development. Additional simulation results show error reductions
by a factor of three.Comment: 73rd Meeting of the Acoustical Society of Americ
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
Geometric Interpretation of Theoretical Bounds for RSS-based Source Localization with Uncertain Anchor Positions
The Received Signal Strength based source localization can encounter severe
problems originating from uncertain information about the anchor positions in
practice. The anchor positions, although commonly assumed to be precisely known
prior to the source localization, are usually obtained using previous
estimation algorithm such as GPS. This previous estimation procedure produces
anchor positions with limited accuracy that result in degradations of the
source localization algorithm and topology uncertainty. We have recently
addressed the problem with a joint estimation framework that jointly estimates
the unknown source and uncertain anchors positions and derived the theoretical
limits of the framework. This paper extends the authors previous work on the
theoretical performance bounds of the joint localization framework with
appropriate geometric interpretation of the overall problem exploiting the
properties of semi-definiteness and symmetry of the Fisher Information Matrix
and the Cram{\`e}r-Rao Lower Bound and using Information and Error Ellipses,
respectively. The numerical results aim to illustrate and discuss the
usefulness of the geometric interpretation. They provide in-depth insight into
the geometrical properties of the joint localization problem underlining the
various possibilities for practical design of efficient localization
algorithms.Comment: 30 pages, 15 figure
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