Tracking Dynamic Boundary Fronts using Range Sensors

1
Tracking Dynamic Boundary Fronts using Range
Sensors

• Subhasri Duttagupta (Ph. D student), Prof. Krithi
  Ramamritham
• Dept of Computer Sc. Engg, Indian Institute of
  Technology,
• Bombay, India

2
Early Warning System For Landslide Prediction
using Sensor Networks
Traffic Management on Highways
3
Tracking Boundary Fronts

• Compute confidence band with high accuracy.
• d Width of the band
• Estimate band with minimum communication overheads

When is the tornado going to hit the city?
Manfredi et al. 2005
k, loss of coverage
Boundary Front Tracking
n, d
n number of observations
4
Combining Spatial and Temporal Estimation at a
location
Spatial Estimation How to estimate Temporal
Estimation When to update
no
Observation
Temporal Estimation
change gt threshold
yes
Spatial Estimation
Multiple Observations
Feedback improves the accuracy of Temporal
Estimation
Feedback from Spatial
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Placement of Estimation Points
regions with high variance

• Goal Minimize LOC of interpolated band
• Start with a small set of equidistant points and
  perform spatial estimation at these points
• Add more estimation points in the region of high
  variance (variance implies spatial variation)

Prediction Error Function can represent LOC
without the knowledge of actual boundary
6
Comparison of DBTR, SE, TE

• DBTR performs better by 2-4
• DBTR utilizes benefits of both the techniques
• Difference in accuracy does not change with d.

• Spatial Estimation provides more accuracy for
  lower d
• Temporal Estimation has better accuracy for
  larger d

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Conclusions

• Tracking dynamic boundary fronts using range
  sensors
• DBTR tracks both spatial and temporal variations
  with low communication overheads
• Spatial estimation technique uses kernel
  smoothing to reduce the effect of noise
• Temporal estimation technique uses Kalman
  filter model-based approach updates estimate
  before the boundary moves out of confidence band

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DBTR Dynamic Boundary Tracking

• Spatial variations captured using spatial
  estimation
• Temporal variations captured using temporal
  estimation
• Interpolation over estimates at k estimation
  points

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Location of Spatial Estimation (SE) and Temporal
Estimation (TE)
h neighborhood
actual boundary
TE(xp2 )
SE(xp1, xp2 )
SE(xp1 )
TE(xp1 )
Sensing nodes
xp2
xp1
Cluster heads