A filtering method developed to improve GNSS receiver data quality

in the CALIBRA project

Luca Spogli1, Vincenzo Romano1,2, Giorgiana De Franceschi1, Lucilla Alfonsi1, Eleftherios Plakidis1, Claudio Cesaroni1, Marcio Aquino2, Alan Dodson2, Joao

Francisco Galera Monico3, Bruno Vani3, Ítalo Tsuchiya3

1Istituto Nazionale di Geofisica e Vulcanologia Rome, Italy

2University of Nottingham, Nottingham, UK

3Departamento de Cartografia, Universidade Estadual Paulista Júlio de Mesquita Filho

3rd TRANSMIT Workshop, 19 – 20 Feb 2014, Torino, Italy

Outline

• Introduction – Multipath and scintillation

• Data and Method – The case of the CIGALA/CALIBRA network – Introducing the Standalone OutLiers IDentIfication Filtering

analYsis technique (SOLIDIFY)

• Improvement of the scientific results with SOLIDIFY

• Remarks

Scintillation and Multipath

Courtesy of NASA Scintillation: «ionsopheric multipath» Signal scattered with environment (buildings, trees, etc.) could mimic scintillation.

Question is: how to tackle the non-ionospheric effects induced by the multipath at ground?

Standard procedure: remove observation below 20° of elevation.

<20°

>20°

Question is: how to tackle the non-ionospheric effects induced by the multipath at ground?

Standard procedure: remove observation below 20° of elevation.

<20°

>20°

PRO’s • Almost good for all multipath sources

• If the site has been well selected • Easy to do at data analysis level (1 “IF”

in the code) • “Fast” computation

• Widely used in GNSS applications

CON’s • Arbitrary • Site-independent • Not really standard: 15° and 30° also used. • Lot of measurements are discarded even if “good”

• Crucial in case of regions not well supplied by necessary logistics (e.g. forests, deserts, etc.) and/or environmental reasons (e.g. oceans)

The case of the CIGALA/CALIBRA network Characterize the Brazilian ionosphere at a global scale:

•2012 data •GPS + GLONASS •L1 frequency

Percentage of data converge (2012)

PolaRxS

Total # of observation: 52666084 Total # of obs. after cut: 33008435 % of data rejected: 37.3%

Percentage of day of available data (2012)

Standalone OutLiers IDentIfication Filtering analYsis technique (SOLIDIFY)

Elev>20° No Elev cut

MANA

σCCSTDDEV

distribution

Philosophy: not all the low elevation measurement are “noisy”, let’s remove the contribution of the outliers (CCSTDDEV) Dierendonck 1993. Method: Ground Based Scintillation Climatology (GBSC)

<σCCSTDDEV>+1.5 IQR

25% 25%

1.5 “Mild Outliers”

Data analysis theory

Standalone OutLiers IDentIfication Filtering analYsis technique (SOLIDIFY)

Elev>20° No Elev cut

MANA

σCCSTDDEV

distribution

Philosophy: not all the low elevation measurement are “noisy”, let’s remove the contribution of the outliers (CCSTDDEV) Dierendonck 1993. Method: Ground Based Scintillation Climatology (GBSC)

<σCCSTDDEV>+1.5 IQR

MANA filtered

1.5 “Mild Outliers”

Data analysis theory

Data filtering for field of view enlargement

% of rejected data with SOLIDFY % of rejected data with 20° elevation cut

Overall % of data rejected: 15.8% % of data rejected with 20° cut: 37.3%

PRO’s • Almost good for all multipath sources

• If the site has been well selected • Easy to do at data analysis level (1 “IF”

in the code) • “Fast” computation

• Widely used in GNSS applications

CON’s • Arbitrary • Site-independent • Not really standard: 15° and 30° also used. • Lot of measurements are discarded even if “good”

• Crucial in case of regions not well supplied by necessary logistics (e.g. forests, deserts, etc.) and/or environmental reasons (e.g. oceans)

PRO’s • Site-dependent • Standardized • Adaptive in time for any environmental

modifications • Good measurements are kept, • Field of view spanned by the receiver is

enlarged • Helps in planning the installation of

additional new receivers

CON’s • No physical assumption, but only based on

general theory of data analysis • Not yet standard in the GNSS community • Validation and refeinement are needed

20° cut

SOLIDIFY

GBSC climatology results

Map of S4 percentage of occurrence above 0.25 in geographic coordinates (GPS + GLONASS, L1 frequency) in the UT range 22-04 UT.

Map of S4 percentage of occurrence above 0.25 in UT vs Latitude (GPS + GLONASS, L1 frequency).

Mutual impact of the northern and southern crest of the EIA in the post sunset hours

Improvement of the scientific results with SOLIDIFY

• The SOLIDIFY technique has been developed for PolaRxS (and GISTM) data

• SOLIDFY relies on the Ground Based Scintillation Climatology (GBSC)

–Thank to SOLIDIFY the number of rejected observations is reduced of a factor of 2.4 (from

37.3% to 15.8%) with respect to the standard cutoff reducing significantly the data loss and

enlarging the field of view.

– This method optimizes the capability of GNSS networks in general

– Helps in planning the installation of additional new receivers aiming to enlarge network

coverage.

• SOLIDIFY has been applied to method to the CIGALA/CALIBRA network data in 2012, increasing its

capability to depict the ionospheric features.

Bibliography GBSC - Spogli et al., A. Climatology of GPS ionospheric scintillations over high and mid-latitude European regions. Ann. Geophys. 27, 3429–3437,2009. - Alfonsi et al. Bipolar climatology of GPS ionospheric scintillation at solar minimum. Radio Sci. 46, RS0D05, 2011. - Romano et al. GNSS station characterisation for ionospheric scintillation applications, Advances in Space Research 52 (2013) 1237–1246. - Spogli et al., Assessing the GNSS scintillation climate over Brazil under increasing solar activity, JASTP, 105-106 (2013) 199–206 Outliers analysis - Barnett, V., Lewis, T., Outliers in Statistical Data. Wiley, 3rd Edition, 1995. - Last, M. and A. Kandel (2004). "Automated detection of outliers in Real-World Data" Ber-Garion University of the Neger and University of South Florida.