27-08-2012, 11:09 AM
Network Localization and Navigation via Cooperation
[attachment=32799]
ABSTRACT
Network localization and navigation give rise
to a new paradigm for communications and contextual
data collection, enabling a variety of new
applications that rely on position information of
mobile nodes (agents). The performance of such
networks can be significantly improved via the
use of cooperation. Therefore, a deep understanding
of information exchange and cooperation
in the network is crucial for the design of
location-aware networks. This article presents an
exploration of cooperative network localization
and navigation from a theoretical foundation to
applications, covering technologies and spatiotemporal
cooperative algorithms.
PRELIMINARIES AND APPLICATIONS
The availability of real-time high-accuracy location-
awareness is essential for current and future
wireless applications. Reliable localization and
navigation is a critical component for a diverse
set of applications including logistics, security
tracking, medical services, search and rescue
operations, control of home appliances, automotive
safety, and military systems, as well as a
large set of emerging wireless sensor network
(WSN) applications. Other applications that
exploit position information include networking
protocols (geo-routing) and interference avoidance
techniques in future cognitive radios. New
market opportunities in real-time location system
(RTLS) technology have an expected value
of $6 billion in 2017.1
THEORETICAL FOUNDATION
The concept of cooperation has been applied to
WSNs, where distributed sensors work together
to draw a consensus about the environment or to
estimate a spatio-temporal process based on
their local measurements [6]. Analogously, network
localization and navigation allow agents to
help each other in estimating their positions,
offering additional benefits such as improved
localization accuracy, resilience to system failure,
increase in coverage, and reduction of cost per
node [2–4]. Understanding the fundamentals of
network localization and navigation via cooperation
is important not only to provide a performance
benchmark, but also to guide algorithm
development and network design.
Consider a network with anchors and agents,
where each of the Na agents is equipped with
multiple sensors that can provide intra- and
inter-node measurements (e.g., using IMU and
RMU, respectively) for the purpose of localization
and navigation. Using these intra- and internode
measurements, represented by z = [zself
zrel], the agents aim to infer their positions x =
[x1 x2 ⋅⋅⋅ xNa]. The accuracy of location estimates
is inherently limited due to random phenomena
affecting z, and fundamental limits of such accuracy
have been derived using the information
inequality [4].
NETWORK INFRASTRUCTURE
GPS is currently the most important and widely
used technology to provide location awareness
around the globe. Through a constellation of
GPS satellites, it provides positioning accuracy
on the order of meters in open outdoor areas,
but fails in harsh operating environments such as
in buildings and urban canyons. In these GPSchallenged
or even GPS-denied environments,
terrestrial localization systems are increasingly
more important. Such systems are currently
based on cellular networks, wireless local area
networks (WLANs), WSNs, and radio frequency
identification (RFID) networks. Each of them
exhibits different performance and has unique
infrastructure requirements. As a case of interest,
in harsh indoor environments, WLANs typically
suffer impairments from surrounding objects
such as furniture and people, whereas narrowband
sensors and RFIDs typically require high
node density to achieve the desired accuracy and
coverage.
LOCALIZATION AND
NAVIGATION ALGORITHMS
The task of network localization and navigation
algorithms is to determine positions from measurements
(observations) and prior knowledge.
From a Bayesian perspective, this task amounts
to determining the posterior distribution p(x|z),
also referred to as the positional belief. Once this
belief is obtained, point estimates can be computed
by determining the mean or mode, leading
to minimum mean square error (MMSE) or maximum
a posteriori (MAP) estimates, respectively.
The primary tools for obtaining such posterior
distributions from measurements and prior
knowledge are Bayes’ rule and marginalization.
Bayes’ rule serves to update beliefs based on new
observations, while marginalization reduces the
dimension of the inference problem.
REMARKS
Network localization and navigation by spatiotemporal
cooperation open the door to a variety
of important, some seemingly inconceivable,
applications that rely on position information.
Firefighters tracking each other in a smoke-filled
building, soldiers determining each other’s position
in harsh environments, medical staff locating
equipment or each other in a busy hospital.
[attachment=32799]
ABSTRACT
Network localization and navigation give rise
to a new paradigm for communications and contextual
data collection, enabling a variety of new
applications that rely on position information of
mobile nodes (agents). The performance of such
networks can be significantly improved via the
use of cooperation. Therefore, a deep understanding
of information exchange and cooperation
in the network is crucial for the design of
location-aware networks. This article presents an
exploration of cooperative network localization
and navigation from a theoretical foundation to
applications, covering technologies and spatiotemporal
cooperative algorithms.
PRELIMINARIES AND APPLICATIONS
The availability of real-time high-accuracy location-
awareness is essential for current and future
wireless applications. Reliable localization and
navigation is a critical component for a diverse
set of applications including logistics, security
tracking, medical services, search and rescue
operations, control of home appliances, automotive
safety, and military systems, as well as a
large set of emerging wireless sensor network
(WSN) applications. Other applications that
exploit position information include networking
protocols (geo-routing) and interference avoidance
techniques in future cognitive radios. New
market opportunities in real-time location system
(RTLS) technology have an expected value
of $6 billion in 2017.1
THEORETICAL FOUNDATION
The concept of cooperation has been applied to
WSNs, where distributed sensors work together
to draw a consensus about the environment or to
estimate a spatio-temporal process based on
their local measurements [6]. Analogously, network
localization and navigation allow agents to
help each other in estimating their positions,
offering additional benefits such as improved
localization accuracy, resilience to system failure,
increase in coverage, and reduction of cost per
node [2–4]. Understanding the fundamentals of
network localization and navigation via cooperation
is important not only to provide a performance
benchmark, but also to guide algorithm
development and network design.
Consider a network with anchors and agents,
where each of the Na agents is equipped with
multiple sensors that can provide intra- and
inter-node measurements (e.g., using IMU and
RMU, respectively) for the purpose of localization
and navigation. Using these intra- and internode
measurements, represented by z = [zself
zrel], the agents aim to infer their positions x =
[x1 x2 ⋅⋅⋅ xNa]. The accuracy of location estimates
is inherently limited due to random phenomena
affecting z, and fundamental limits of such accuracy
have been derived using the information
inequality [4].
NETWORK INFRASTRUCTURE
GPS is currently the most important and widely
used technology to provide location awareness
around the globe. Through a constellation of
GPS satellites, it provides positioning accuracy
on the order of meters in open outdoor areas,
but fails in harsh operating environments such as
in buildings and urban canyons. In these GPSchallenged
or even GPS-denied environments,
terrestrial localization systems are increasingly
more important. Such systems are currently
based on cellular networks, wireless local area
networks (WLANs), WSNs, and radio frequency
identification (RFID) networks. Each of them
exhibits different performance and has unique
infrastructure requirements. As a case of interest,
in harsh indoor environments, WLANs typically
suffer impairments from surrounding objects
such as furniture and people, whereas narrowband
sensors and RFIDs typically require high
node density to achieve the desired accuracy and
coverage.
LOCALIZATION AND
NAVIGATION ALGORITHMS
The task of network localization and navigation
algorithms is to determine positions from measurements
(observations) and prior knowledge.
From a Bayesian perspective, this task amounts
to determining the posterior distribution p(x|z),
also referred to as the positional belief. Once this
belief is obtained, point estimates can be computed
by determining the mean or mode, leading
to minimum mean square error (MMSE) or maximum
a posteriori (MAP) estimates, respectively.
The primary tools for obtaining such posterior
distributions from measurements and prior
knowledge are Bayes’ rule and marginalization.
Bayes’ rule serves to update beliefs based on new
observations, while marginalization reduces the
dimension of the inference problem.
REMARKS
Network localization and navigation by spatiotemporal
cooperation open the door to a variety
of important, some seemingly inconceivable,
applications that rely on position information.
Firefighters tracking each other in a smoke-filled
building, soldiers determining each other’s position
in harsh environments, medical staff locating
equipment or each other in a busy hospital.