29,450 research outputs found
SEQUENTIAL LOCALIZATION OF SENSOR NETWORKS
The sensor network localization problem with distance information is to determine the positions of all sensors in a network, given the positions of some of the sensors and the distances between some pairs of sensors. A definition is given for a sensor network in the plane to be "sequentially localizable." It is shown that the graph of a sequentially localizable network must have a "bilateration ordering," and a polynomial time algorithm is given for deciding whether or not a network's graph has such an ordering. A provably correct algorithm is given which consists of solving a sequence of quadratic equations, and it is shown that the algorithm can localize any localizable network in the plane whose graph has a bilateration ordering.
Distributed Maximum Likelihood Sensor Network Localization
We propose a class of convex relaxations to solve the sensor network
localization problem, based on a maximum likelihood (ML) formulation. This
class, as well as the tightness of the relaxations, depends on the noise
probability density function (PDF) of the collected measurements. We derive a
computational efficient edge-based version of this ML convex relaxation class
and we design a distributed algorithm that enables the sensor nodes to solve
these edge-based convex programs locally by communicating only with their close
neighbors. This algorithm relies on the alternating direction method of
multipliers (ADMM), it converges to the centralized solution, it can run
asynchronously, and it is computation error-resilient. Finally, we compare our
proposed distributed scheme with other available methods, both analytically and
numerically, and we argue the added value of ADMM, especially for large-scale
networks
Recovery of Localization Errors in Sensor Networks using Inter-Agent Measurements
A practical challenge which arises in the operation of sensor networks is the
presence of sensor faults, biases, or adversarial attacks, which can lead to
significant errors incurring in the localization of the agents, thereby
undermining the security and performance of the network. We consider the
problem of identifying and correcting the localization errors using inter-agent
measurements, such as the distances or bearings from one agent to another,
which can serve as a redundant source of information about the sensor network's
configuration. The problem is solved by searching for a block sparse solution
to an underdetermined system of equations, where the sparsity is introduced via
the fact that the number of localization errors is typically much lesser than
the total number of agents. Unlike the existing works, our proposed method does
not require the knowledge of the identities of the anchors, i.e., the agents
that do not have localization errors. We characterize the necessary and
sufficient conditions on the sensor network configuration under which a given
number of localization errors can be uniquely identified and corrected using
the proposed method. The applicability of our results is demonstrated
numerically by processing inter-agent distance measurements using a sequential
convex programming (SCP) algorithm to identify the localization errors in a
sensor network
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
Sigma Point Belief Propagation
The sigma point (SP) filter, also known as unscented Kalman filter, is an
attractive alternative to the extended Kalman filter and the particle filter.
Here, we extend the SP filter to nonsequential Bayesian inference corresponding
to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a
low-complexity approximation of the belief propagation (BP) message passing
scheme. SPBP achieves approximate marginalizations of posterior distributions
corresponding to (generally) loopy factor graphs. It is well suited for
decentralized inference because of its low communication requirements. For a
decentralized, dynamic sensor localization problem, we demonstrate that SPBP
can outperform nonparametric (particle-based) BP while requiring significantly
less computations and communications.Comment: 5 pages, 1 figur
A New Distributed Localization Method for Sensor Networks
This paper studies the problem of determining the sensor locations in a large
sensor network using relative distance (range) measurements only. Our work
follows from a seminal paper by Khan et al. [1] where a distributed algorithm,
known as DILOC, for sensor localization is given using the barycentric
coordinate. A main limitation of the DILOC algorithm is that all sensor nodes
must be inside the convex hull of the anchor nodes. In this paper, we consider
a general sensor network without the convex hull assumption, which incurs
challenges in determining the sign pattern of the barycentric coordinate. A
criterion is developed to address this issue based on available distance
measurements. Also, a new distributed algorithm is proposed to guarantee the
asymptotic localization of all localizable sensor nodes
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