1. Zach Zira & Kevin Roy ISE 533
GPS Comparison Study
Zach Zira & Kevin Roy
ISE 533

2. Overview About GPS The Experiment Our Hypothesis The Data ANOVA
Normal Plots
Residual Plots
Main Effects
Conclusion
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3. About GPS More than 24 GPS satellites are currently orbiting the earth
Traveling 8,700 mph (3.9 km/sec) at an average height of 12,500 miles (20,200 km)
Guaranteed to have signals of at least four satellites (at any location at any time)
Circulation time of 12 h sidereal time = 11 h 58 min earth time
There is a lot more other different types of satellites (communication, weather, etc.)
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4. GPS Locations Day 2 Day 1 11:56 12:00 Day 3 Day 4 11:52 11:48
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5. GPS Error Sources: Atmospheric effects Shifts in the satellite orbits
Errors of the satellite clocks
Multipath effect
Rounding
Describe how the coordinates move
Describe why this contributed to picking this topic
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**most to least significant

6. The Experiment Set three GPS units next to each other
A+, GSAT, and TomTom Mkii
Log the output via Bluetooth
Repeat (1 & 2) 11 hrs 58 minutes later
Conduct the process for a total of 3 times
GPS devices don’t interfere with each other
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8. Our Hypothesis Using Latin Square design Statistical Model:
Row effect (α): GPS Device
Column effect (β): Time
Treatment effect (τ): Day
Statistical Model:
yijk = µ + α i + τj + βk + εijk
Hypothesis:
9 hypothesis tests (3 for lat, 3 for long, 3 for number of satellites)
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9. Data 11/12/2018 Column Response 1 2 Row A+ day 1 day 2 day 3 Gsat
Column
Response 1 2
Row A+
day 1
day 2
day 3
Gsat
TomTom
Time
Latitude 1 2
Device A+
Gsat
TomTom
Time
NOS 1 2
Device A+ 11 9
Gsat 8
TomTom
Time
Longitude 1 2
Device A+
Gsat
TomTom
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10. ANOVA ← The device is significant when observing longitude
General Linear Model: Long versus Device, Time, Day
Factor Type Levels Values
Device fixed , 2, 3
Time fixed , 1, 2
Day fixed , 2, 3
Analysis of Variance for Long, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
Device
Time
Day
Error
Total
S = R-Sq = 98.10% R-Sq(adj) = 92.41%
← The device is significant when observing longitude
General Linear Model: Sat versus Device, Time, Day
Factor Type Levels Values
Device fixed , 2, 3
Time fixed , 1, 2
Day fixed , 2, 3
Analysis of Variance for Sat, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
Device
Time
Day
Error
Total
S = R-Sq = 86.54% R-Sq(adj) = 46.15%
Nothing is significant when → observing number of satellites
General Linear Model: Lat versus Device, Time, Day
Factor Type Levels Values
Device fixed , 2, 3
Time fixed , 1, 2
Day fixed , 2, 3
Analysis of Variance for Lat, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
Device
Time
Day
Error
Total
S = R-Sq = 86.89% R-Sq(adj) = 47.57%
← Nothing is significant when observing latitude
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11. Normal Plots Questionable normal probability plots
All normal probability plots passed equal variance test
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12. Residual Plots

13. Main Effects
With additional number of satellite data, we could have done a regression analysis
Determine if there is correlation between the number of satellites and the variance of the locations.
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14. Conclusion Significant Not significant
Longitude vs Device (p= 0.024)
Moderately significant:
Longitude vs Day (p= 0.115)
Not significant
All other factors were not significant
From our analysis, the GPS did not behave as predicted
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15. Questions?
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16. Wavier
Please do not use any of the information in this presentation or in the term paper for publication without written consent from both Zach Zira and Kevin Roy