0. ION GNSS 2007 Session D1: Algorithms and Methods 1 September 26,2007
Flying™ RTK Solution as Effective Enhancement of Conventional Float RTK Dmitry Kozlov, Gleb Zyryanov Magellan, Russia
ION GNSS 2007
Session D1: Algorithms and Methods 1
September 26,2007

1. Scope (18 slides)
Summary
Float and Fixed RTK
Flying RTK
Magellan products with Flying RTK
Convergence performance
Concluding remarks
Acknowledgment

2. Flying RTK algorithm: Summary
We present new RTK algorithm which:
Can be positioned between standard Float and Fixed RTK
Has all the external attributes of Float RTK
Uses internally some ideas of Fixed RTK
Insures convergence performance better than Float RTK
Integrated into 2 latest Magellan products
OEM DG14 RTK board
Handheld ProMark3 Surveyor

3. Float and Fixed RTK: difference
Float RTK
Fixed RTK
Receiver type
Usually L1 only
Typically L1&L2
Accuracy level
Meter to decimeter
Centimeter
Dependence on baseline length
Not so dramatical
Noticeable (working distance is usually less than 50 km)
Initialization time
Decimeter after minutes
Centimeter after some convergence time depending on baseline
Treating DD ambiguity
As float unknown value
As known integer value

4. Float and Fixed RTK: commonality
Float RTK
Fixed RTK
Core processing engine
Kalman Filter or similar recurrent estimator
Input data
Receiver raw observations and external reference RTK data
Treating ambiguity
Ambiguity is processed as float all the time
Ambiguity is processed as float initially
Behavior at star up
Slow convergence
Same convergence until ambiguity fix
Fixing ambiguity to integer
Not appled
Applied, but by quite a separated search and validation procedure

5. Float and Fixed RTK: tentative diagram
Float RTK
Fixed RTK

6. Flying RTK: backlog
Actual DD is unknown integer (finite number of alternatives)
There is theoretical foundation how to treat it
Optimal multi-channel algorithm is too complicated
Let us use Float RTK scheme
Let us process DD ambiguity as unknown float, but consider as unknown integer
Let us use DD ambiguity search results to generate Flying RTK correction to Float RTK solution

7. Fixed and Flying RTK: tentative diagram
Fixed RTK
Flying RTK

8. RTK slogans
Float RTK: always process DD ambiguity as unknown float variable
Fixed RTK: first process DD ambiguity as unknown float variable, then (after fix) as known integer value
Flying RTK: always process DD ambigity as unknown float variable, but always consider as unknown integer

9. Different RTK: typical convergence tubes
Flying RTK convergence is as flat as Float RTK convergence
Flying RTK converges faster than Float RTK
Flying RTK never fixes wrong ambiguity as it can be with Fixed RTK
Flying RTK steady state accuracy can be as good as cm

10. Flying RTK implementation: DG14 RTK
L1 GPS+SBAS RTK OEM board (base and rover)
Fixed and Flying RTK
RTCM-2.3 (base and rover)
RTCM-3.0 and Magellan proprietary (rover)
20 Hz Raw data
10 Hz RTK (with extrapolated base) position
5 Hz synchronized (matched tags) RTK position
OTF Fixed RTK initialization
RTK with moving base
Heading function

11. Flying RTK implementation: ProMark3 RTK
L1 GPS+SBAS RTK handheld (base and rover)
Real time and post-processed Surveying and Mobile Mapping functions
Fixed and Flying RTK
RTCM-3.0 (base and rover)
RTCM-2.3,3.0 and Magellan proprietary (rover)
Compatibility with VRS, FKP and MAC Networks
NTRIP&DIP rover (with external GPRS module)
External license free radio (base and rover)
OTF Fixed RTK initialization
Initialization of known point
Initialization on kinematics bar

12. Flying RTK performance: evaluation principles
Apple to apple comparison
PC version of RTK
Static data but kinematics processing
RTK auto-reset each 10 minutes
Statistically sufficient estimates
CEP convergence pattern
Cases:
DG14 data (base and rover)
Zmax data (base and rover, L1 portion only)
ProMark3 data (against Ntrip Network)

13. Flying RTK: Convergence with DG RTK data
DG14 base and rover
24 full days data at different times and locations
Baselines from few meters to 10 km
Open sky to partly shaded sky environment
Kinematics processing

14. Flying RTK: convergence with Zmax data (L1 only)
Zmax data: base and rover
L1 CA portion only
Open sky baselines
Baselines from 7 to 52 km
>48 hours for each baseline
Accuracy after 3 minutes
Kinematics processing

15. Flying RTK: convergence with ProMark3 data
ProMark3 data (rover)
Orpheon NTRIP Network, France
10 km baseline
Open sky
48 hours
Kinematics processing

16. Conclusions
Many GNSS users want decimeter accuracy
Standard DGPS can deliver only sub-meter
One of the choices is RTK
L1/L2 RTK with fixed ambiguity can be too expensive
Alternative is much cheaper Float L1 RTK
L1 RTK Float convergence is not always fast
Flying RTK algorithm shows improvement compared to classic Float RTK ‘in the same box’
Flying L1 RTK is good compromise for these users

17. Acknowledgments
Magellan System Test group:
for their careful testing and validation efforts with release DG14 RTK and ProMark3 RTK
Yves Le Pallec, Eugeny Sunitsky (all Magellan), and
Bill Cottrell (Cottrell Navigation Services):
for help with data collection

18. THAHK YOU FOR YOU ATTENTION
Final slide
Flying™ RTK Solution as Effective Enhancement of Conventional Float RTK
THAHK YOU FOR YOU ATTENTION
QUESTIONS?