11,356 research outputs found
Cooperative and Distributed Localization for Wireless Sensor Networks in Multipath Environments
We consider the problem of sensor localization in a wireless network in a
multipath environment, where time and angle of arrival information are
available at each sensor. We propose a distributed algorithm based on belief
propagation, which allows sensors to cooperatively self-localize with respect
to one single anchor in a multihop network. The algorithm has low overhead and
is scalable. Simulations show that although the network is loopy, the proposed
algorithm converges, and achieves good localization accuracy
Distributed localization of a RF target in NLOS environments
We propose a novel distributed expectation maximization (EM) method for
non-cooperative RF device localization using a wireless sensor network. We
consider the scenario where few or no sensors receive line-of-sight signals
from the target. In the case of non-line-of-sight signals, the signal path
consists of a single reflection between the transmitter and receiver. Each
sensor is able to measure the time difference of arrival of the target's signal
with respect to a reference sensor, as well as the angle of arrival of the
target's signal. We derive a distributed EM algorithm where each node makes use
of its local information to compute summary statistics, and then shares these
statistics with its neighbors to improve its estimate of the target
localization. Since all the measurements need not be centralized at a single
location, the spectrum usage can be significantly reduced. The distributed
algorithm also allows for increased robustness of the sensor network in the
case of node failures. We show that our distributed algorithm converges, and
simulation results suggest that our method achieves an accuracy close to the
centralized EM algorithm. We apply the distributed EM algorithm to a set of
experimental measurements with a network of four nodes, which confirm that the
algorithm is able to localize a RF target in a realistic non-line-of-sight
scenario.Comment: 30 pages, 11 figure
A survey of localization in wireless sensor network
Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network
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
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