non line of sight error detection in mobile communication by nisha menon k

1. Non Line of sight
problem in Mobile
Location estimation
NISHA MENON K
ROLL NO:16
FISAT
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2. 2
OUTLINE
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Location estimation
NLOS error
NLOS identification
Residual Analysis
LOS reconstruction
Conclusion

3. Operator services
Billing
Network management
Location based
services
Assistance
Roadside
assistance
Personal or vehicle
emergency
Alarm management
Driving Directions
Tracking
Tracking criminals
Tracking external
resources containers
Monitoring
Monitoring delivery
process
Fleet & freight
tracking
Personal Child
Security
Information
Nearest service
Traffic
navigation help
Information
Directory
LOCATION ESTIMATION: APPLICATION
AREAS
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4. Angle of arrival (AOA)
Time difference of arrival
(TDOA)
Enhanced observed time
difference (EOTD)
GPS Tracking
4
LOCATIONESTIMATION:DIFFERENTMETHODS
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5. Locating mobile terminal in 2
dimensions
requires the measurement of the LOS distance between the
mobile and at least three participating BSs
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6. 6
NLOSerror
SCATTERING
Range measurements corrupted by
error
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7. NLOS error
• The major error sources in the mobile location include
Gaussian measurement noise and non-line-of-sight (NLOS)
propagation error, the latter being the dominant factor.
• NLOS error translates the mobile’s location estimate to a
biased estimate
• This problem has been recognized as a critical issue,
possibly a “killer issue” for mobile location.
• In order to mitigate the effect of the measurement bias, it
is necessary to develop location algorithms that are robust
to the NLOS error.
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8. PROBLEM FORMULATION
• BSs measure the time of arrival of a signal that has been
sent to the mobile and then transponded back to the
network.
• The arrival times are then converted to range
measurements.
• ‘m’th range is modelled as
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9. NLOS identification
• It is not known which apriori range measurements (if any)
contain NLOS errors.
• The NLOS measurements can be identified,
• At each BS, the range measurements are first smoothed by
modeling using Nth order polynomial fit
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time history of the range measurement from each
BS
our apriori knowledge of the standard deviation
of the Gaussian noise

10. NLOS identification
• Smooth it as
• 2 hypothesis H0: NLOS is absent (only los component)
• H1: NLOS is present
• NLOS identification technique requires comparison of the
standard deviation of a sample statistic to the known standard
deviation of that statistic under the null hypothesis that the
measurements are LOS.
• The standard measurement noise is modeled as a zero-mean
random variable
• Noise variance
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11. NLOS identification
.
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If the NLOS error is present along with nm (t) then the measured
range have a significantly larger average deviation from the
smoothed curve than
standard deviation of a sample
statistic

12. NLOS identification
• When the NLOS error is present, then the measured range will
deviate from the smoothed curves on the average by
• The presence of a large standard deviation will be used to
discriminate between LOS versus NLOS measurements.
• We reject the null hypothesis, Ho, (LOS case) for large values
of
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13. Residual analysis rank test
• we assume that the LOS hypothesis has been rejected at
one or more BSs but that there is some uncertainty about
the hypothesis testing results.
• we can confirm our rejection of the null hypothesis by
using
a residual analysis rank test.
• Residual is defined as the difference between the measured
range, rm(ti), and the calculated range, as follows:
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14. Residual analysis rank test
Assume some BSs have LOS
Use all measurements at ti to estimate Lm(ti)
Calculate residual:
Calculate the number of times is larger than the
residue of BSs that we believe to have LOS
with the mobile for each ti
And it is termed as rank. If rank >0 confirm rejection of H0
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15. LOS RECONSTRUCTION
As range measurement is corrupted by the NLOS error, we
employ a NLOS error correction technique for LOS
reconstruction
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1
• smooth the data using an Nth order polynomial fit
under the assumption that the major effect of the NLOS
error is to just to bias the data
2
• calculate the deviation of each value of the measured
range from the polynomial fit at each instant of time,
rm(ti) - Sm(ti) which equals Lm(ti)-am
3
• LOS measurements were produced by adding am
+rm(ti)-Sm(ti) at each instant of time.

16. Conclusion
• Presented a new tracking algorithm that is capable of
discriminating between LOS versus NLOS range
measurements and correcting the NLOS error.
• Marilynn P, Wylie and Jack Holtzman, “The non-line of
sight problem in mobile location estimation”, in Proc. IEEE
International Conference on Universal Personal
Communication (Vol:2 ) pp. 827–831, 1996
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Reference

17. THANK YOU
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