Position-Location Networks (U-PoLoNets)
This project is
funded by the National Science Foundation (NSF) under Grant #0515019.
simply referred to as UWB) has some unique advantages over traditional
The received pulses are
relatively immune to the multipath effects that narrowband systems
suffer from, due to the fine time resolution provided by UWB signals,
which allows the resolution of multipath components. Additionally, UWB
signals have strong material penetration capabilities (particularly in
the lower bands approved by the FCC), which is desirable in indoor
Due to the short duration of
the pulses, UWB can be used for precise positioning and tracking of
devices and offers the potential to fuse communications, positioning
and sensing functionalities.
UWB has the potential to
support high data rates while appearing noise-like to other RF
technologies, and offers inherent data security due to covertness with
applications in battlefield communications.
the following, we describe the
applications of Position-Location Networks (PoLoNets), where UWB is
envisioned as an ideal physical layer (PHY) solution.
been a desired feature in many commercial and military applications.
Navigation was the primary use of position information with man-made
systems being used as early as the 1950’s (e.g., Loran-C
Navigation system) and exploding with the advent of the Global
Positioning System (GPS). More recently, position location has been an
active area of research in many areas including cellular E-911, sensor
networks, ad hoc networks, robotics, and ubiquitous computing. Current
applications of PoLoNets include inventory control, home automation,
safety networks, tracking personal items, personnel monitoring, command
and control in emergency situations, the guidance of robots in remote
locations, and many others. In fact, the IEEE 802.15.4a standard, a
standard for low power, low-data rate wireless is primarily focused on
position location applications.
environments, accurate position information can be obtained
GPS. However, there are many situations where GPS is either unreliable
(e.g., indoor scenarios), or impractical (e.g., where GPS receivers are
too bulky or expensive), requiring the development of other solutions.
As one example, consider the command and control of a firefighter
operation where multiple personnel are deployed into a building. For
safety and efficiency purposes, it would be extremely helpful for a
command center outside the building to be established for not only
communication but also position tracking as shown in the Figure below.
In such a case, we require an ad hoc position location and
communication network that is independent of GPS and is not reliant on
goal of such a network would be
firefighter with awareness of his own location,
command-and-control with knowledge
of each firefighter’s location,
the exchange of short
messages between firefighters and command-and-control.
networks extend location-awareness within the area of interest, in
addition to serving as a communications network. Traditional ranging
(and position location) applications have relied on optical (laser),
ultrasound, or narrowband RF physical layers. It is well known that
optical and ultrasound have limited range in harsh environments and may
fail completely when the line-of-sight (LOS) is blocked. Additionally,
narrowband RF solutions have difficulty in dense multipath due to
severe multipath fading. However, UWB is an excellent physical layer
solution because of its usefulness in harsh multipath environments,
material penetration capabilities, its ability to fuse accurate
position-location with low-data rate communication and its covertness
for tactical applications. Due to the nature of the indoor propagation
environment and the power restrictions on UWB systems, in order to
provide the mentioned features over a sizable area, we would require a
multi-hop network of nodes that serve as a location network. The
following section provides the description of a network solution that
can be used for the discussed application.
A specific network
architecture that can
be used for the described application is shown above . The network
consists of a small number of “location-aware” or
fixed anchors located outside the area of interest.
locations of fixed anchors maybe be available via GPS or by setting up
a local coordinate system.
from the setup instant, the network evolves in two
1: Nodes called propagated
anchors or reference nodes, whose locations are unknown a priori,
deployed in the region of interest2.
The deployed reference nodes, depending on the available connectivity
to other nodes, trilaterate their locations using range estimates from
fixed anchors or other reference nodes whose locations have already
been estimated. Reference nodes that estimate their location provide
range-estimates to other unlocalized reference or mobile nodes. Each
“layer” of location-aware reference nodes serves as
source of range information for the subsequent layer, thereby
“propagating” location-awareness, even in the
direct connectivity with fixed anchor nodes. Alternatively, unlocalized
reference nodes could share their connectivity/range information, both
to fixed anchors and among themselves, and jointly estimate their
locations. This is commonly referred to as collaborative position
2: After the reference nodes
have estimated their own locations by ranging to one another or to
fixed anchors, the second phase of the network involves assisting any
mobile node that enters the area by providing a framework to estimate
its location. Mobile nodes, depending on their location and available
connectivity, communicate with a subset of fixed anchors and/or
reference nodes whose locations have been estimated in order to obtain
range information. These range estimates are used to trilaterate their
locations. Additionally, in this phase the reference nodes provide a
multi-hop communication network to relay the mobiles’
information to, and short messages from, the data-sink
this manner, through the network of reference nodes, (i)
location-awareness is propagated from the fixed anchors located outside
the area of interest to the mobile nodes within the area of interest,
and (ii) mobile node location information is passed from the mobile
nodes to the data-sink. It is important to point out the shift in
emphasis on location-estimation in PoLoNets vis-a-vis traditional
sensor and mobile ad hoc networks. In sensor and ad hoc networks,
location-estimates are typically used in order to improve performance
of the medium access control (MAC) and routing algorithms, but are not
the main objectives of the PoLoNets.
can view a PoLoNet as a generic sensor network where the physical
parameter being sensed is the location of the mobile nodes.
However, unlike sensor networks, the expected lifetime of the PoLoNets
may be limited (e.g., position location in emergency scenarios) and
thus energy efficiency is not always the primary focus. On the other
hand, PoLoNets are different from typical mobile ad hoc networks where
large quantities of data may have to be transported across the network
with a certain Quality-of-Service (QOS) while minimizing latency. In
contrast, in PoLoNets, (i) while energy efficiency may be one metric of
interest, in a majority of cases localization accuracy3
, reliability of communication,
survivability and scalability may take priority over energy efficiency
and (ii) brief messages are assumed to be exchanged between the nodes
of the network at low data rates.
networks described in the previous
subsection can be classified on the basis of infrastructure,
range-information, synchronization and the computational capabilities
of the nodes.
- Infrastructure-based vs. Ad hoc reference
node network: In
the case where the network of reference nodes is deployed in the area
of interest in advance, with each reference node placed in a known
location (thereby serving as a fixed anchor within the area of
interest), such a network is called an infrastructure-based network. It
must be noted that in most cases it may not be possible to have an
infrastructure of stationary location-aware reference nodes within the
area of interest a priori. In such a case, reference nodes can be
deployed at the time of use in an ad hoc fashion.
- Range Information: Range information of
can be used to estimate node locations. The most important categories
are (i) Time-of-Arrival (TOA)-based range estimates, (ii)
Time-Difference-of-Arrival (TDOA) based range estimates, (iii) Received
Signal Strength (RSS) based range estimates and (iv) Connectivity based
range information. UWB signals can be used for accurate TOA-based range
estimation. RSS-based are typically used in narrowband sensor networks.
- Synchronous vs. Asynchronous: If all nodes
share a common
clock, they are said to be synchronous; if each node possesses a unique
clock, they are said to be asynchronous.
- Centralized vs. Distributed Solver: Due to
constraints on the
hardware complexity or limited connectivity of the radios, it may not
be possible for each node to estimate its location. Therefore, range
and location information have to be routed to a centralized
“location-solver”. If all nodes are capable of
their coordinates, then we call such a scenario a distributed solver as
opposed to a centralized solver.
1. A node is said to be
“location-aware” or “localized”
if its location
is known or can be estimated based on available range information. A
node whose location is unknown and cannot be estimated is said to be
2. Deployment options are
not considered here but reference nodes could either be pre-existing,
deployed manually as in a fire-fighter scenario, via tiny robots,
dispersed via UAV, or launched into the area of interest.
3. For instance, in
a fire-fighter  position-tracking system, the knowledge of whether a
firefighter is on one side of a door or the other, could be critical.
The research in this project can be divided
into two parts.
In the first
part, we focus on the aforementioned Phase 2 and the main objective
is the design and modeling of ad hoc
position-location networks, in which position information is propagated
through a network of reference nodes in order to track the locations of
mobile nodes. This creates a framework for the tracking of mobile nodes
and well as a multi-hop message-passing infrastructure between mobile
nodes and control nodes located outside the area of deployment. The
main goal is to derive design principles and analytical models for the
performance of such networks that serve as useful tools in the
development of practical solutions.
The second part of this project mainly
focuses on Phase 1,
i.e., estimating the locations of unlocalized reference nodes within
the area of interest. This is the key step of establishing a
PoLoNet and the achieved localization accuracy has an immediate impact
on the subsequent location estimation of mobile nodes in Phase 2. We
are particularly interested in exploiting the idea
of collaborative network position location, as it has been shown to
hold the potential to both increase the location coverage and
localization accuracy, especially in harsh environments such as low
inter-node connectivity and NLOS-dense propagation conditions. Our main objective is, via different
theoretical analysis and
algorithm evaluation, to understand the fundamental role of node
collaboration and ultimately develop algorithms to utilize node
collaboration in a more effective way.
of research and
Ranging and near-ground propagation measurements:
results confirming accurate ranging capabilities using UWB signals.
of the performance of practical range estimators using UWB multipath
of the accuracy of practical range estimators in LOS and NLOS
indoor near-ground UWB propagation measurements.
Bounds and Performance of various practical Location Estimators.
of different parameters on localization accuracy.
into other design areas from the perspective of localization accuracy.
Design in U-PoLoNets:
a new time-hopping spread spectrum MAC protocol.
with CSMA from the perspective of localization accuracy based on
simulations under realistic scenarios.
Control in U-PoLoNets:
adaptive power control schemes based on localization-accuracy to
improve robustness of location estimates.
identification and mitigation:
a technique to identify NLOS measurement using signal statistical
the impact of the NLOS propagation environment on localization
a new linear-programming approach that mitigates the effect of NLOS
propagation and outperforms least-squares location estimators.
the propagation of localization error:
the impact of the propagation of localization error.
a novel multi-hop bounding based technique to mitigate the propagation
of localization error.
- Examined a
technique to improve GPS-provided position solutions to nodes based on
measurements taken in forests.
a collaborative quasi-linear programming approach to handle both NLOS
mitigation and node collaboration.
and examined a method to localize a network of nodes based on global
nonlinear optimization using GPS-provided node positions as initial
a probabilistic position location framework. Examined its efficacy in
distinguishing localizable and un-localizable nodes. Proposed an NLOS
mitigation technique and demonstrated its effectiveness for
probabilistic position location.
improved and more realistic CRLB:
a new CRLB based on a distance-dependent SNR modeling, which directly
relates inter-node distance to the final localization accuracy.
the new CRLB to interpret the role of node collaboration and
demonstrated that the improvement on localization accuracy from node
collaboration beyond 3-hop diminishes.
software for the evaluation and testing of the performance of protocols
and algorithms implemented on ad hoc U-PoLoNets.
graphic user interface (GUI) to evaluate the performance of different
collaborative position location algorithms.
of findings resulting from these activities:
Ranging and near-ground propagation measurements:
Indoor UWB measurement results were
conducted in order to
demonstrate the accuracy of UWB time-of-arrival (TOA) based ranging.
Using practical low-complexity TOA estimators, range errors of less
than 5 centimeters were observed at a distance of 5 meters. Analysis
and simulation of practical range estimators were carried out to arrive
at models for range measurement errors in U-PoLoNets. Specifically,
applying the energy-threshold range estimator to the measurement data,
we derived mathematical models for both line-of-sight (LOS) and
non-line-of-sight (NLOS) range estimates (see Figures 1 and 2). These
models are used later to evaluate position location algorithms.
1: Theoretical PDF
the estimated histogram of LOS range estimates.
Figure 2: Theoretical PDF and
the estimated histogram of NLOS range estimates.
also conducted time-domain
measurements of the indoor near-ground UWB channel and determine
characteristics from the data [C2, C10]. We compared the
middle-ground (MG) and above-ground (AG) signal propagations and
observed that as
the antenna height decreases, the path loss, shadowing variance
which is consistent with existing results, as shown in Figures 3 and 4,
respectively. Small scale channel
characteristics, on the other hand, do not show a straightforward
respect to antenna height.
3: Measurement data and
fitted path loss for (a) LOS; (b) NLOS.
Figure 4: Measurement data and
fitted shadowing for (a) LOS; (b) NLOS.
Based on the range error models obtained in Item 1 above, we
studied bounds on localization accuracy and the performance of various
practical location estimators. The impact of different network
parameters such as node density, geometry (see Figure 6), and the
accuracy of range estimates was studied and insights into other design
areas such as multiple-access and power-control schemes from the
perspective of localization accuracy were developed [J3]. It was found
that the number of range estimates available plays a crucial role in
determining localization accuracy (see Figure 5), and that increasing
the number of available range estimates improves the average
localization accuracy. Further, it was demonstrated that practical
estimators such as the least-squares estimator exhibit similar trends
(see Figure 5).
5: The effect of node geometry on
Figure 6: The performance of the LS estimator
versus the CRLB.
Design in U-PoLoNets:
Based on the findings in Item 2 above, a new
multiple-access access scheme was devised [C1] that ensures a high
throughput of range estimates to unlocalized nodes, thereby ensuring
high localization accuracy. This scheme relies on the assignment of
time-hop codes to different transmitting nodes and was designed for the
case where the network (distributed, ad hoc PoLoNet) is asynchronous.
Using the U-PoLoNet simulator, this scheme was shown to outperform the
carrier-sense multiple access (CSMA) protocol with respect to
localization accuracy (Figure 7) as a function of time. Further, it was
verified that the improvement achieved by the proposed scheme over the
CSMA-based scheme in terms of localization accuracy can be attributed
to a higher throughput of range estimates (Figure 8), confirming the
insights gained from Item 2.
of the simulation
of the deployment of the proposed multiple access
a U-PoLoNet with the described architecture can be found here.
comparison of the performance of the proposed protocol and CSMA in
terms of the average network localization
time for different values of the path
loss exponent. We see that the network localization error decreases
for the proposed approach than for the CSMA scheme.
Figure 8: A comparison of the performance of the
proposed protocol and CSMA in terms of the average number of ranging
packets received successfully within the network versus time. The
proposed approach clearly has a much h igher effective throughput of
Control in U-PoLoNets:
From Item 2, it was observed that the
localization error of a
node is dependent on the geometry of reference nodes, connectivity with
localized reference nodes and range estimate variances (which depend on
the distances between nodes from Item 1). As a mobile node moves
through a network of reference nodes, all these quantities can vary
with time and therefore, the localization error of a mobile node
fluctuates with the progression of time. Figure 9 illustrates an
example of the variation of a mobile node’s localization
it moves through an area containing randomly distributed reference
nodes. The localization error fluctuation is analogous to the spatial
fading of received signal power in wireless propagation channels. An
adaptive power control scheme based on localization-accuracy was
developed [C5] which makes the obtained location estimates more robust
(Figure 10) and accurate. This power control scheme attempts to
determine the optimal transmit power [C5] that an unlocalized node
to use to ensure a given localization accuracy.
Figure 9: “Spatial
Fading” of Localization Accuracy. The localization error of the
mobile node fluctuates with time due to the variation of the relative
geometry of reference nodes, connectivity with localized reference
nodes and range estimate variances.
Figure 10: upper: Trajectory of Mobile Nodes, and lower: Reduction in
“Spatial Fading” of localization error using power control.
identification and mitigation:
The NLOS propagation environment is known
(from previous work
in cellular systems) to severely degrade the accuracy of TOA-based
range estimates. Since significant envisioned applications of
U-PoLoNets rely on indoor location tracking, the indoor
non-line-of-sight (NLOS) propagation environment poses a formidable
challenge in attaining the requisite localization accuracy. We first
developed a technique to identify NLOS range estimates based on the
received signal statistics [J2]. With this knowledge, a new
linear-programming (LP) approach [J1,C3], was devised which mitigates
the impact of the NLOS propagation environment on localization
accuracy. The efficacy of this scheme was demonstrated in the
simulation of a U-PoLoNet deployed in a dense multipath environment
(Figure 11) where it was observed that the proposed approach not only
mitigates the effect of NLOS propagation, but can take advantage of
biased NLOS range estimates to improve localization accuracy (Figure
12). This is in contrast to the least-squares (LS) estimator, whose
performance degrades if NLOS range estimates are incorporated without
mitigation of bias errors (Figure 12).
of a sample
simulation of NLOS mitigation can be found here.
Figure 11: 2D NLOS
mitigation in a NLOS propagation environment: The mobile node moves
through the network of anchor nodes at a speed v = 2.5 m/s.
Every Ts=1 second, the mobile computes its location
based on available range estimates.
Figure 12: Variation of the localization error
obtained using different approaches with time. On the average, the LP
approaches outperform the LS approaches.
- Limiting the propagation of localization error:
a network of unlocalized nodes, extending the location coverage, i.e.,
localize as many nodes as possible, is a key task. A simple way to
is to use sequential location estimation. We studied the various ways
increasing the area over which the desired localization accuracy can be
guaranteed when sequential estimation is used: (i) improving range
accuracy, (ii) using a superior (with minimal bias) location estimator,
increasing node density, (iv) increasing transmit power, (v) improving
geometry of anchor nodes, (vi) using methods to mitigate the
error. A novel method of mitigating the propagation of localization
on linear-programming that incorporates NLOS range estimates was
proposed [C7]. This can be seens from Figures 13 and 14.
Figure 13: In the LOS environment,
the sequential LP outperforms the sequential LS estimator due to the
multihop bouding in limiting the propagation of localization error.
Figure 14: In the NLOS environment, the
sequential LP again outperforms the sequential LS estimator and the
performance improvement increases as the portion of NLOS range
- Collaborative Position Location:
We evaluated a technique for improving the localization
on the global positioning system (GPS) for networks of nodes in harsh
and demonstrate its efficacy via a combination of simulations and
in forests [C8]. Specifically, we create a system of range equations
network connectivity and solve this system of nonlinear equations using
least squares (LS) optimization technique, with any available GPS
as the initial estimate. Based on our simulations and measurements, the
improved technique results in localization accuracy in forests that is
on par with
clear-field reference GPS measurements.
15: The localization error of the nonlinear optimization based
approach, compared to positioning error of GPS-alone system, versus the
number of collaborating sensors. As can be seen, the more collaborating
nodes, the smaller the localization error.
Figure 16: The
localization error of the nonlinear optimization based approach,
compared to positioning error of GPS-alone system, versus the amout of
noise in the range estimates. As the UWB range estimates
become worse, the improvement over GPS solution becomes smaller.
abovementioned technique needs centralized computation in order to
solve the system of nonlinear equations. We
developed a distributed collaborative quasi-linear programming (CQLP)
to deal with both NLOS mitigation and node collaboration [C6]. The
is able to handle the degenerate cases in the LP method and
increases the location coverage. The improvement can be seen from
Figures 17 and 18.
17: The location coverage of the proposed CQLP approach and the
sequential LS estimator. The number of hops indicates the degree to
which nodes are collaborating to each other. It is observed that the
CQLP significantly increases the locatin coverage and the improvement
diminishes as the number of hops is greatler than two.
Figure 18: The localization error of the
proposed CQLP approach and the sequential LS estimator. It is observed
that the CQLP with three-hop node collaboration outperforms the
sequential LS estimator. The localization accuracy improves as the
number of hops inceases, i.e., more nodes are collaborating,.
of some limitations associated with existing algorithms in terms of
addressing both node localizability and efficiently incorporating node
collaboration, we investigated the framework probabilistic position
location. Probabilistic position location differs from existing work in
the sense that position solution is no longer a one-shot solution. In
stead, a set of possible position solutions, namely particles, is
returned and each solution is associated with a probability measure
quantifying the uncertain about that solution. We demonstrate that this
framework is effective in identifying localizable and un-localizable
nodes (Figure 19). In addition, we developed a simple NLOS mitigation
technique, which incorporates spatial constraints to the particle
updating procedure, and greatly improves the convergence result in
NLOS-dense environment (Figure 20).
Figure 19: Illustration of
localizability under the framework of probabilistic position location.
As shown, particles of the node on the left converged well to within a
small area, thus is considered as being localizable. On the other hand,
particles of the node on the right are spread over a larger area,
therefore will not be regarded as localizable.
Figure 20: Illustration of the
effect of properly exploit NLOS range estimates
under the framework of probabilistic position localization. The left
figure shows that, without considering the NLOS range estimates as
shown by the dashed line, particles cannot converge well due to the
outlier in these range estimates (Note that those particles with
negligible probabilities are not plotted.). On the other hand, the
right figure shows that, with a simple procedure to properly exploit
NLOS range estimates, particles converge well to their true locations
and the localization accuracy can be greatly improved.
work on probabilistic position location has either high
computationaly complexity or has not considered practical problems such
as NLOS measurements. In lieu of the above results, we are currently
developing a probabilistic position location algorithm that can better
utilize node collaboration as well as exploit NLOS range estimates,
especially for NLOS-dense and low-connectivity environments. In
addition, we are also developing methods to incorporate other
information such as angle-of-arrival (AOA) and altimeter readings into
our probabilistic position location framework.
- An improved CRLB for indoor collaborative position
many algorithms that have been developed for collaborative position
location, a theoretical framework explaining the fundamental limits and
exact role of node collaboration is still not available. Traditional
metric such as Cramer-Rao lower bound (CRLB) is not perfectly suitable
for this task. In this work, we derived a new CRLB based on a
distance-dependent signal-to-noise ratio (SNR) modeling, which
equivalently relates the range estimation noise to inter-node distance
[C9]. We believe this is a more appropriate model for indoor position
location where the difference in inter-node distances could lead to
significant difference in the amount of noise to be seen at different
range estimates. The fact that the new CRLB is lower than the existing
CRLB corroborates our belief that this readily-available knowledge
should be exploited in designing indoor position location
algorithm. Figure 21 below shows that the new CRLB is in general
lower than the old CRLB and the two are almost the same when the path
loss exponent is 2. Figure 22 shows that there is still a performance
gap between the popular LS estimator and the CRLB. We are currently
exploring the possibility of using the new CRLB to investigate the
fundamental role of node collaboration.
Figure 21: The comparison of the
new and the old CRLBs. As shown, the new CRLB
is lower than the old one. The difference is significant for a large
path loss exponenet, while negligible for a small path loss exponent.
Figure 22: The localization error of the LS
estimator versus the number of anchors, compared to the new and the old
CRLBs. Obviously, there is some gap between the performance of the
LS estimator and what is predicted by the CRLBs.
- An integrated graphic user interface (GUI):
developed an integrated GUI for evaluating different collaborative
position location algorithms (Figure 23). The platform provides a
uniform interface and simulation settings that can be used to
conveniently assess the performance of different algorithms with the
help of specially designed visualization and statistics collections
23: An integrated GUI for developing and evaluating different
collaborative position location algortihms.
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