ION GNSS 2012
Session C4: GNSS Ground Based Augmentation Systems (GBAS)

Title: Data Quality Improvements and Applications of Long-Term Monitoring of Ionospheric Anomalies for GBAS
Author(s): S. Jung, M. Kim, J. Lee, Korea Advanced Institute of Science and Technology, South Korea; S. Pullen, Stanford University; J. Gillespie, Federal Aviation Administration
Date/Time: Thursday, September 20, 2012, 4:00 p.m.
Room: 206 (NCC)

An automated Long-Term Ionospheric Anomaly Monitoring (LTIAM) software package has been developed to support continuous ionospheric monitoring for the U.S. Local Area Augmentation System (LAAS) developed by the U.S. Federal Aviation Administration (FAA) in the Conterminous U.S. (CONUS). Continuous monitoring is needed to confirm the long-term validity of the ionospheric anomaly threat model [1,3] and update it if necessary. This is of particular importance over the next few years, as the intensity of solar storms is expected to peak in 2013-15. Prior work [2,4] has demonstrated that the LTIAM not only identifies extremely large ionospheric gradients but also supplies reliable ionospheric gradient statistics under all conditions. This tool can also be utilized to build ionosphere threat models for all regions where Ground-Based Augmentation Systems (GBAS) will be fielded in the future.

The LTIAM software enables the post-processing of data continuously collected by GPS reference station networks. Ionospheric gradients over short-baseline distances of 5 - 40 km can be observed using data collected from the Continuously Operating Reference Stations (CORS) network, which has over 1800 stations as of 2011 compared to about 400 stations prior to 2004. However, as the total number of stations increases, the number of stations with poor GPS data quality also increases. This is caused by the differing types of CORS receivers and antennas fielded by multiple organizations in various environments; some good, some not-so-good. The use of poor-quality data degrades the accuracy of ionospheric delay estimates and produces too many faulty anomaly candidates. Thus, station selection criteria need to be defined to reduce processing time in both automated procedures and manual analysis and validation while maximizing the benefit of this tool.

This paper presents a comprehensive method of GPS data quality determination to select CORS stations with high-quality data for the purpose of ionospheric anomaly monitoring. A series of data-quality-measurement algorithms provide information about receiver cycle slips, multipath on code and phase observations, receiver signal-to-noise ratios, receiver noise, and the daily number of observations (including measurement gaps). Cycle slip detection methods already developed as a part of LTIAM pre-processing have been upgraded by incorporating cycle slips detected using multipath estimates. Multipath on code observations is computed by linear combinations of L1 C/A-code, L1 P-code, and L2 P-code observations. Carrier multipath and receiver noise are estimated using simple stochastic models and a least-squares estimation method.

Using these algorithms, a subset of CORS stations are selected to optimally meet three criteria: geographical distribution, GPS data quality, and data sampling rate. To observe anomalous ionospheric gradients in CONUS, the selected stations should cover all of CONUS with separations of less than 40 km to the degree possible. To meet this criterion in regions with relatively few stations, some degree of data quality may need to be sacrificed. Also, CORS network stations provide data with different sampling rates of 1, 5, 10, 15, or 30 seconds. Data with higher sampling rates is preferred to observe ionospheric gradients accurately and to validate anomalous events using L1 code-minus-carrier measurements [2,4]. However, if a station provides high-quality data, and its location increases the geographic observability of ionospheric behavior, it should be selected despite a low data rate. This paper presents the list of CORS stations selected for use in the LTIAM and shows several examples of stations used and not used to demonstrate the station-selection method. Using the resulting subset of stations, it also shows results from processing recently-collected ionospheric data.

Now that the LTIAM uses a subset of well-distributed, high-quality CORS stations, it is easier to distinguish actual ionospheric anomalies from those created by faulty measurements. Over time, this will allow us to better estimate the prior probabilities of anomalous ionospheric gradients and use this information to better understand the underlying threat and refine the GBAS threat models [5]. The bounds of the current threat model for CONUS are based on a handful of validated observations made on a single day and time (20 November 2003) in northern Ohio [1,3]. Many more validated observations have been made of lower (but still anomalous) gradients. This paper describes a method for estimating and progressively updating the probabilities of anomalous gradients of several magnitudes (less than 200 mm/km, between 200 to 300 mm/km, between 300 to 400 mm/km, and?greater than 400 mm/km) and ground speeds (less than100 m/s, between 100 to 300 m/s, between 300 to 750 m/s, and greater than 750 m/s) using LTIAM results from future measurements. We expect that these estimates will become useful by the latter half of this decade, after the peak of the current solar cycle passes. References:

[1] Datta-Barua, S., Lee, J., Pullen, S., Luo, M., Ene, A., Qiu, D., Zhang G., and Enge, P., "Ionospheric Threat Parameterization for Local Area GPS-Based Aircraft Landing Systems," AIAA Journal of Aircraft, Vol. 47, No. 4, Jul. 2010, pp. 1141-1151. [2] Lee, J., Jung, S., Bang, E., Pullen, S., and Enge, P., "Long Term Monitoring of Ionospheric Anomalies to Support the Local Area Augmentation System," Proceedings of ION GNSS 2010, Portland, OR, Sept. 21-24, 2010. [3] Pullen, S., Park, Y.S., and Enge, P., "Impact and mitigation of ionospheric anomalies on ground-based augmentation of GNSS," Radio Science, Vol. 44, RS0A21, Aug. 2009. [4] Lee, J., Jung, S., and Pullen, S., "Enhancements of Long Term Ionospheric Anomaly monitoring for the Ground-Based Augmentation Systems," Proceedings of ION ITM 2011, San Diego, CA, Jan. 24-26, 2010. [5] Pullen, S., Rife, J., and Enge, P., "Prior Probability Model Development to Support System Safety Verification in the Presence of Anomalies," Proceedings of IEEE/ION PLANS 2006, San Diego, CA, June 25-27, 2006.

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