1. Open Loop Tracking of GPS Radio Occultation for LEOs
Chris Bombach*, Changyong Cao, Ph.D.**
* University of Texas at El Paso
** NOAA/NESDIS/STAR

2. Overview
Radio occultation of GPS signals received by LEO satellites is an atmospheric sounding technique for retrieving atmospheric profiles from the troposphere via refractivity.
This requires a high degree in accuracy to retrieve reliable data.
This information is retrieved using the excess phase between the GPS satellite and a LEO satellite.
GPS radio occultation shows a depiction quickly and accurately.
Allows for vertical scanning of successive layers of the atmosphere.

3. Radio Occultation
Radio occultation is atmospheric sounding technique that uses GPS signals to get a physical profile of the atmosphere a change in GPS signal.

4. Significance to JPSS
These radio occultation profiles are important for numerical weather prediction because they are stable, accurate, and act as anchors for other types of data for assimilation.
This is due to the consistency at which data can be collected and at certain times.
The JPSS program is considering adding this capability to a future satellite, and STAR is developing new capabilities to support radio occultation missions in the areas of data processing, quality control and monitoring, and algorithm development.

5. Objective
Develop a procedure and algorithm for open loop tracking of GPS signals which would enable the data processing from level 0 to level 1 and 2 to retrieve atmospheric profiles.
This requires extracting the residual phase, which is the excess phase generated from refractivity in the atmosphere.

6. Problems
There are limitations in the payload which constrains the antenna size and therefore the signal to noise ratio (SNR), and the ability to penetrate to the lower levels of atmosphere.
The traditional method of locking onto a signal, a phase lock loop, is inaccurate at low SNR.

7. SNR’s Effect on Phase Shifts

8. Changing Geometry The residual phase depends heavily on the geometry.
As the distance between satellites changes there will be a delay.
This requires the use of interpolation and measuring the delay between transmission to receiver to get an accurate estimate of the distance between them.

9. Doppler Effect
Since the data can’t be measured directly, more processing is required, this includes identifying the Doppler shifts in the measured phase.
These shifts are the information needed to feed into further algorithms.

10. Doppler Effect

11. Tracking Methods
The open loop tracking method uses received signal and compares it to independently modeled data, compensates for discontinuities, and derives the final excess phase.
This is in contrast to more traditional methods.
Open Loop tracking is a promising method to help alleviate the SNR problem.

12. Signal Discontinuities/Cycle Slips
They are when there is a rapid change in data received. The reference data does not have these.
These lead to bad data points and need to be detected and remedied.

13. Cross Correlation
To check for delays and unusable data the measured and model were cross correlated and normalized.

14. Cross Correlation

15. Really Bad Errors

16. Error Before Correction

17. Full comparison

18. Low SNR comparison

19. High SNR comparison

20. Residual Phase
This is the extracted residual phase

21. Conclusion
Open loop tracking is a viable method for finding Doppler frequency shifts and compensating for cycle slips. At low SNRs there is still a problem but after 60 samples (1.2 seconds) discontinuities become far less of a problem and the retrieved residual phase becomes more accurate.

22. Future Works
More investigation on how to deal with cycle slips and the effect of SNR overall.
A method to compare to other instruments’ measurements.
Further this information along to get bending angles and the exact refractory index.
Coding Concerns:
Much of the used algorithm is in Matlab and needs to be converted to C++; mostly the phase unwrapping portion and interpolation techniques
Is currently taking ASCII data inputs and has hardcoded file names and paths
Fortran90 implementation of cross correlated data is outright wrong and it needs a better method for calculating the boundary at where significant correlation exists.