Statistical framework for estimating GNSS bias

Abstract

Abstract. We present a statistical framework for estimating global navigation satellite system (GNSS) non-ionospheric differential time delay bias. The biases are estimated by examining differences of measured line-integrated electron densities (total electron content: TEC) that are scaled to equivalent vertical integrated densities. The spatiotemporal variability, instrumentation-dependent errors, and errors due to inaccurate ionospheric altitude profile assumptions are modeled as structure functions. These structure functions determine how the TEC differences are weighted in the linear least-squares minimization procedure, which is used to produce the bias estimates. A method for automatic detection and removal of outlier measurements that do not fit into a model of receiver bias is also described. The same statistical framework can be used for a single receiver station, but it also scales to a large global network of receivers. In addition to the Global Positioning System (GPS), the method is also applicable to other dual-frequency GNSS systems, such as GLONASS (Globalnaya Navigazionnaya Sputnikovaya Sistema). The use of the framework is demonstrated in practice through several examples. A specific implementation of the methods presented here is used to compute GPS receiver biases for measurements in the MIT Haystack Madrigal distributed database system. Results of the new algorithm are compared with the current MIT Haystack Observatory MAPGPS (MIT Automated Processing of GPS) bias determination algorithm. The new method is found to produce estimates of receiver bias that have reduced day-to-day variability and more consistent coincident vertical TEC values.

Subauroral and Polar Traveling Ionospheric Disturbances During the 7–9 September 2017 Storms

“…This TID study utilizes GNSS TEC observational data generated at MIT Haystack Observatory (Rideout & Coster, 2006;Vierinen et al, 2016) and archived in the Madrigal database (http://www.openmadrigal.org). TID analysis is based on differential TEC processing as described in Coster et al (2017) and Zhang et al (2019 along with extensive discussion regarding the detrending method and the use of different sliding window lengths.…”

Observations of ion‐neutral coupling associated with strong electrodynamic disturbances during the 2015 St. Patrick's Day storm

“…Additional observations involved in this study include global GPS total electron content (TEC) maps, Millstone Hill and Arecibo FPI neutral winds, and DMSP in situ plasma drift measurements. GPS TEC data are from MIT Haystack Observatory's GPS TEC analysis package [Rideout and Coster, 2006], upgraded recently with improved bias estimation [Vierinen et al, 2016]. Millstone Hill FPI data used for this particular event are a subset of the data used in Zhang et al [2015], and descriptions on them are provided in the paper and references therein.…”

Finding multiscale connectivity in our geospace observational system: Network analysis of total electron content

“…Figure 6a shows heightened C D j in the Northern Hemisphere under neutral and +B Z (middle and top rows) at MLATs between ∼50 and 80 ∘ throughout most dayside MLTs, peaking in the ∼60-72 ∘ noon MLT sector. These are regions that statistically map to the polar cusp and magnetospheric boundary layers at MLATs >∼70 ∘ and the central plasma sheet at MLATs between ∼60 and 70 ∘ [Vasyliunas, 1979;Newell et al, 1991;Newell and Meng, 1992]. A future study incorporating magnetospheric information in combination with TEC data using multivariate network analyses will allow further investigation of these potential relationships.…”

“…We process TEC data during January and June 2016 obtained from the Madrigal upper atmospheric science database (http://madrigal3.haystack.mit.edu/), which compiles data from the worldwide system of groundbased GPS receivers into 1 ∘ latitude × 1 ∘ longitude geographic coordinate distributions at 5 min cadence [Rideout and Coster, 2006;Vierinen et al, 2016]. Madrigal automated processing uses a mapping function to project line-of-sight TEC to the vertical and accounts for various unwanted effects in the signal data, including loss of lock, receiver and transmitter bias, low elevation angles, and outliers.…”