Transcript Slides

Presented by:
Chaitanya K. Sambhara
Paper by: Maarten Ditzel, Caspar Lageweg,
Johan Janssen, Arne Theil TNO Defence,
Security and Safety, The Hague, The Netherland
Introduction
• We discuss the problem of target tracking.
• Target Tracking :
• Current Position
• Estimated Position.
Aim
• To study the tracking of several objects in
a sensor network simultaneously and
focus on trade-offs between the amount of
communication in the network and tracking
accuracy.
Main Goal
• The main goal is to investigate various
approaches to reduce the number of
required messages while achieving a
certain track accuracy.
What is the target ?
• The target can be a moving vehicle, for
example, or can be a phenomenon such
as an approaching fire.
• It is assumed that each individual sensor
node is equipped with appropriate sensory
device(s) to be able to detect the target as
well as to estimate its distance based on
the sensed data.
How it works
• The sensors that are triggered by the
target collaborate to localize the target in
the physical space to predict its course.
Assumptions
• Sensor nodes are scattered randomly in a
geographical region. Each node is aware
of its location.
• Absolute location information is not
needed. It is sufficient for the nodes to
know their location with respect to a
common reference point.
Assumptions for location
lnformation
• In their experiment the sensor nodes are
stationary and we have directly encoded
the location information into the sensor
nodes to eliminate the possibility of any
localization error.
• Hence there is no emphasis on any
particular localization technique.
Assumptions
• The sensors must be capable of
estimating the distance of the target to be
tracked from the sensor readings.
• It is assumed that the sensor has already
learned the sensor reading to distance
mapping.
Tracking a target
• Tracking a target involves three distinct
steps:
• Detecting the presence of the target.
• Determining the direction of motion of the
target.
• Alerting appropriate nodes in the network.
Issues
• Communication of data consumes more
energy than processing.
• Reduction of amount of messages sent
can be achieved by utilizing local
processing.
Data Aggregation
• In data aggregation local sensor readings
are combined to reduce the
communication load of the network.
• This decreases the communication cost by
a considerable amount.
Aggregation Strategies
• 1. Reference
• 2. Differential Messaging
• 3. Local Aggregation
• 4. Local Aggregation and Differential
Messaging
Aggregation Strategies
• #2. Differential Messaging
• Uses local processing
• When the previously sensed target moves,
the observed quality changes.
• Threshold used for quality difference is
▲T
Aggregation Strategies
• #3.
• Data aggregation used.
• Quality observation(normalized strength of
the sensor signal) used by a node among
the group.
Aggregation Strategies
• #4.
• Uses both, differential messaging and
local aggregation
Position Estimation
• Quality of the observation is qi ε [0, 1]
• Each node i observing the target is
assumed to broadcast {xi, qi(t)},
• Where
Position Estimation
• The weight wi(t) of
• each observation is measured as
• The estimated position is
Simulation Environment
• Network Topology:
• Assumed that the wireless sensor network
consists of a 51×51 grid of sensor nodes, placed
100 m apart.
• The communication range rc of the network is
chosen such that nodes can only communicate
with their direct neighbors.
• The sink is located at the center of the network.
Target Trajectory Path
•
is given by:
Algorithms
• The target estimation algorithm is
executed centrally at the sink in
simulations 1 and 2.
• In the simulations 3 and 4 it is run locally
at the nodes.
• The track algorithm is assumed to run at
the sink node.
Tracking
• To determine a target’s trajectory a
Kalman filter is used.
• The Kalman filter is a recursive filter,
which estimates the state of a dynamic
system from a series of noisy
measurements.
Multi Stage Contact-Track Association
• False contacts can result in false tracks.
To reduce the number of false tracks,
multi-stage contact-track association is
applied.
• The technique labels tracks in three
categories: potential, tentative and
confirmed.
Tracker
Results
Target Tracking Results
Strategy 1
Strategy 2
Strategy 3
Strategy 4
Conclusion
• We discussed different aggregation
strategies and analyzed them.
• The results show a relevant trade-off
between the amount of communication
and the performance of the track
algorithm.
Conclusion
• From the results, the local aggregation
without differential messaging (Strategy
#3) shows better cost-performance tradeoff in noisy environments.
Thank You
•
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