MGA Webinar Series : 8
GNSS Raw Data Measurement from Android Device
Dinesh ManandharCenter for Spatial Information Science
The University of Tokyo
Contact Information: dinesh@iis.u-Tokyo.ac.jp
6th Dec 2018
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 1

Android Raw Data Logging APPs
• GNSS Logger• Logs Raw Data
• Some devices also output AGC and Navigation Bit Data
• Multi Band Compatible• Geo++ RINEX Logger• APP to generate RINEX Observation File
• https://play.google.com/store/apps/details?id=de.geopp.rinexlogger&hl=en_US• Dual Frequency Compatible• GNSS Compare• Compares position accuracy from each type of GPS and GALILEO Signal• https://play.google.com/store/apps/details?id=com.galfins.gnss_compare&hl=en_US
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 2

Android Raw Data Logging APP: GnssLogger

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 3

GnssLogger: Sample GNSS Raw Data
Raw,148210058,6108000000,,,-1224572056418544947,0.0,1011000.0,,,0,24,0.0,51,16023402,13,38.61924362182617,-448.32047602682997,0.0021302644163370132,1,2484.2876523853806,0.09621196860735094,1.57542003E9,,,,0,,1,,1.57542003E9
Raw,148210058,6108000000,,,-1224572056418544947,0.0,1011000.0,,,0,24,0.0,16,16023363,1000000000,22.01333236694336,-448.7947882361932,2.99792458E8,6,54362.39162390184,3.4028234663852886E38,1.17645005E9,,,,0,,1,,1.17645005E9
Raw,148210059,6108000000,,,-1224572056418544947,0.0,1011000.0,,,0,2,0.0,99,448838468,42,33.2121467590332,-514.7820368047455,0.4567280495416781,4,2821.165958154149,3.4028234663852886E38,1.59975002E9,,,,0,,3,,1.59975002E9
Raw,148210059,6108000000,,,-1224572056418544947,0.0,1011000.0,,,0,12,0.0,99,451783264,33,36.38795852661133,-789.8168953823033,0.31444507671593813,4,3649.9399078027736,3.4028234663852886E38,1.60143744E9,,,,0,,3,,1.60143744E9
Raw,148210060,6108000000,,,-1224572056418544947,0.0,1011000.0,,,0,11,0.0,99,459913670,33,36.715248107910156,352.6647914612738,0.0026083579286932945,1,-2248.5336107033927,0.0013041789643466473,1.602E9,,,,0,,3,,1.602E9
Raw,148210060,6108000000,,,-1224572056418544947,0.0,1011000.0,,,0,1,0.0,17,720287,71,26.745431900024414,-150.53345126992713,0.749486332694286,4,982.5725209813795,3.4028234663852886E38,1.60256256E9,,,,1,,3,,1.60256256E9
Raw,148210060,6108000000,,,1224572056418544947,0.0,1011000.0,,,0,24,0.0,99,451325376,47,31.866626739501953,540.7229232612153,0.004294544458389282,1,2792.0530589872405,0.0021472
72229194641,1.60312499E9,,,,0,,3,,1.60312499E9
Raw,148210061,6108000000,,,1224572056418544947,0.0,1011000.0,,,0,23,0.0,17,163750,51,30.871082305908203,751.2325553423079,0.561522050942072,4,3454.136294113628,3.40282346638528
86E38,1.60368755E9,,,,0,,3,,1.60368755E9
Raw,148210061,6108000000,,,1224572056418544947,0.0,1011000.0,,,0,17,0.0,99,450599950,39,34.2637939453125,6.408111582737082,0.4097535710026252,4,42.03919027799001,3.402823466385
2886E38,1.60424998E9,,,,0,,3,,1.60424998E9
Raw,148210061,6108000000,,,1224572056418544947,0.0,1011000.0,,,0,8,0.0,17,490263,73,26.511377334594727,305.8143842387426,0.7594304219231991,6,1528.659101239677,3.40282346638528
86E38,1.60537498E9,,,,0,,3,,1.60537498E9
Raw,148210062,6108000000,,,1224572056418544947,0.0,1011000.0,,,0,194,0.0,17,631661,13,38.51543045043945,39.9065635909258,0.002155878348276019,1,221.32303678571114,0.09622477557
332045,1.57542003E9,,,,0,,4,,1.57542003E9
Raw,148210062,6108000000,,,1224572056418544947,0.0,1011000.0,,,0,195,0.0,17,934792,27,29.99894905090332,63.56321905450875,0.6032179424598567,4,356.8051378882135,3.4028234663852
886E38,1.57542003E9,,,,0,,4,,1.57542003E9

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 4

GnssLogger: Sample GNSS Raw Data, Header# # Header Description:# # Version: v2.0.0.1 Platform: 8.1.0 Manufacturer: Xiaomi Model: MI 8##Raw,ElapsedRealtimeMillis,TimeNanos,LeapSecond,TimeUncertaintyNanos,FullBiasNanos,BiasNanos,BiasUncertainty
Nanos,DriftNanosPerSecond,DriftUncertaintyNanosPerSecond,HardwareClockDiscontinuityCount,Svid,TimeOffsetNanos,State,ReceivedSvTimeNanos,ReceivedSvTimeUncertaintyNanos,Cn0DbHz,PseudorangeRateMetersPerSecond,Pseudor
angeRateUncertaintyMetersPerSecond,AccumulatedDeltaRangeState,AccumulatedDeltaRangeMeters,AccumulatedDelt
aRangeUncertaintyMeters,CarrierFrequencyHz,CarrierCycles,CarrierPhase,CarrierPhaseUncertainty,MultipathIndicator,
SnrInDb,ConstellationType,AgcDb,CarrierFrequencyHz# # Fix,Provider,Latitude,Longitude,Altitude,Speed,Accuracy,(UTC)TimeInMs# # Nav,Svid,Type,Status,MessageId,Sub-messageId,Data(Bytes)#

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 5

GnssLogger: Sample GNSS Raw Data, Raw Data
Raw,678357857,828940000000,,,-1227744676059580169,0.0,5.135445098385752,,,0,2,0.0,16431,1504929579420,11,42.886016845703125,253.99448677373584,0.0013739581918343902,1,-230928.61821755476,6.869790959171951E-4,1.57542003E9,,,,0,,1,,1.57542003E9
Raw,678357858,828940000000,,,-1227744676059580169,0.0,5.135445098385752,,,0,5,0.0,16431,1504926917641,12,42.140777587890625,299.9095448909793,0.0014970472548156977,1,-262724.97200484236,7.485236274078488E-4,1.57542003E9,,,,0,,1,,1.57542003E9
Raw,678357858,828940000000,,,1227744676059580169,0.0,5.135445098385752,,,0,6,0.0,16,828010596684,1000000000,36.201961517333984,275.322190
7272733,2.99792458E8,2,1144.5147370874038,3.4028234663852886E38,1.57542003E9,,,,0,,1,,1.57542003E9
Raw,678357858,828940000000,,,-1227744676059580169,0.0,5.135445098385752,,,0,7,0.0,16431,1504921150324,19,34.20191192626953,228.16970128013054,0.003542420221492648,1,213920.67928652398,0.09691804650992876,1.57542003E9,,,,0,,1,,1.57542003E9
Raw,678357858,828940000000,,,1227744676059580169,0.0,5.135445098385752,,,0,9,0.0,16431,1504924621121,19,34.36507797241211,587.10396663023
86,0.0034764972515404224,1,468139.7243548873,0.0017382486257702112,1.57542003E9,,,,0,,1,,1.57542003E9
Raw,678357858,828940000000,,,1227744676059580169,0.0,5.135445098385752,,,0,13,0.0,16431,1504920021810,19,34.32540512084961,666.6443721854
594,0.0032926779240369797,1,561690.3480669406,0.0016463389620184898,1.57542003E9,,,,1,,1,,1.57542003E9
Raw,678357858,828940000000,,,1227744676059580169,0.0,5.135445098385752,,,0,17,0.0,16431,1504916630146,20,33.56485366821289,744.6819117466
221,0.003812001552432776,1,619849.6424447118,0.0019060007762163877,1.57542003E9,,,,1,,1,,1.57542003E9
Raw,678357858,828940000000,,,1227744676059580169,0.0,5.135445098385752,,,0,19,0.0,16431,1504921921584,23,31.828954696655273,735.126564052
538,0.004389062523841858,1,599416.7818672012,0.09734136766110336,1.57542003E9,,,,0,,1,,1.57542003E9
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 6

GnssLogger: Sample GNSS Raw Data, Position and NMEA
Fix,gps,35.850232,139.862279,37.854518,0.008482,4.000000,1543710718999
NMEA,$GPGSV,4,1,14,02,71,324,32,06,60,115,39,05,43,288,35,09,29,045,25*74 NMEA,$GPGSA,A,3,02,05,06,07,09,13,19,29,30,,,,1.6,0.7,1.4*3A,1543710720204,1543710720205
NMEA,$GPGSV,4,2,14,07,26,093,34,19,24,182,23,30,22,130,27,13,22,207,23*72 NMEA,$GNGSA,A,3,02,05,06,07,09,13,19,29,30,,,,1.6,0.7,1.4*24,1543710720204,1543710720205
NMEA,$GPGSV,4,3,14,29,11,323,22,23,04,042,,17,03,169,*4A
NMEA,$GNGSA,A,3,67,68,69,82,83,84,,,,,,,1.6,0.7,1.4*24,1543710720204,1543710720205
NMEA,$GPGSV,4,4,14,06,,,39,09,,,30,30,,,36,8*68
NMEA,$QZGSA,A,3,01,02,03,,,,,,,,,,1.6,0.7,1.4*2B,1543710720204,1543710720206
NMEA,$GLGSV,2,1,07,83,80,264,26,68,65,326,32,82,37,165,23,69,32,254,33*6D NMEA,$IMGSA,A,3,,,,,,,,,,,,,1.6,0.7,1.4*24,1543710720204,1543710720206
NMEA,$GLGSV,2,2,07,67,28,037,24,84,26,329,19,77,08,073,11*5F
NMEA,$BDGSA,A,3,203,,,,,,,,,,,,1.6,0.7,1.4*17,1543710720204,1543710720206
NMEA,$QZGSV,2,1,05,01,83,285,31,03,41,201,33,02,07,171,22*53
NMEA,$GAGSA,A,3,104,109,112,119,,,,,,,,,1.6,0.7,1.4*20,1543710720204,1543710720206
NMEA,$QZGSV,2,2,05,01,,,34,03,,,33,8*71
NMEA,$GPRMC,003159.00,A,3551.013922,N,13951.736758,E,000.0,337.0,02121,1543710720205
8,,,A*51
NMEA,$BDGSV,1,1,02,203,38,224,23,202,20,250,*60,1543710720206,1543710720205
NMEA,$GAGSV,2,1,08,104,75,259,30,112,61,159,30,119,42,045,29,109,22,236,25*6F,1543710720205
NMEA,$GAGSV,2,2,08,104,,,34,112,,,32,119,,,21,109,,,26,1*7A,1543710720205
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 7

GnssLogger: Sample GNSS Raw Data, Navigation Bit Data

Nav,101,769,1,5,9,76,34,58,55,7,116,-65,67,-77,-42,88Nav,102,769,1,5,9,76,34,58,55,7,116,-65,67,-77,-42,88
Nav,103,769,1,5,9,76,34,58,55,7,116,-65,67,-77,-42,88Nav,105,769,1,5,9,76,34,58,55,7,116,-65,67,-77,-42,88
Nav,106,769,1,5,9,76,34,58,55,7,116,-65,67,-77,-42,88

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 8

Android Raw Data Logging APP Geo++ RINEX Logger

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 9

Geo++ RINEX Logger

Supports both L1 & L5

http://www.geopp.de/logging-of-gnss-raw-data-on-android/
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 10

Android Raw Data Logging APP: GNSS CompareCompares Position Output from GPS L1, GPS L5, GALILEO E1 and GALILEO E5A
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 11

Sample Output Data from GNSS Compare: Galileo E1%% Timestamp: timestamp of the logged information%% satID: ID of the used satellited - satID[constellationSize]%% Elev: elevation of the used satellited%% CN0: signal strength of the used satellited%% PR: pseudoranges of the used satellites - pseudoranges[constellationSize]%% Inno: Kalman Filter innovation vector - gamma[constellationSize]%% CovInno: Covariance of the innovation vector S[constellationSize,constellationSize]%% EstimPos: Estimated position - x_meas[numStates]%% CovEstimPos: Covariance of estimated position P_meas[numStates,numStates]%% E: Error of the estimated position w.r.t the FINE location%% Fl, fineLocation.Latitude, fineLocation.Longitude, fineLocation.Altitude

Timestamp,151802.965
satID,E2_E1,E3_E1,E8_E1
Elev,31.610655657172565,79.62218334355671,26.434511304778855
CN0,24.7218017578125,9.973297119140625,16.62681007385254
PR,2.5750447126294035E7,2.3339095558620986E7,2.42679284542776E7
Inno,-91.24294137954712,18.01171224936843,64.18829656392336
CovInno,12.22970286249292,14.444868432192381,12.690663461967336
EstimPos,-1538772.0345251851,6187985.963390375,147515.7288238819,74.3935243591933,0.5047784960376713
CovEstimPos,2.9416686630197995,15.169400083974876,0.10437474374131087,7.460593341167593,0.16107177414031879
FL, 1.334501, 103.963689, 69.899166
Timestamp,151803.984
satID,E2_E1,E3_E1,E8_E1
Elev,31.60793506699695,79.62530160159848,26.4394654099114
CN0,24.383716583251953,9.113094329833984,18.569250106811523
PR,2.5750681578115076E7,2.3339045816195916E7,2.4267482347606793E7
Inno,-85.14420802891254,14.95177936181426,64.21592565998435
CovInno,12.229701651449918,12.83295614164091,12.69720039697161
EstimPos,-1538774.9123953995,6187978.210011118,147515.98560067688,79.99688261846154,0.10867092664722788
CovEstimPos,3.24440551434881,12.963002889258968,0.10430843724528373,7.
4033699984902706,0.1646876458097381

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 12

Sample Output Data from GNSS Compare: Galileo E5a%% Timestamp: timestamp of the logged information%% satID: ID of the used satellited - satID[constellationSize]%% Elev: elevation of the used satellited%% CN0: signal strength of the used satellited%% PR: pseudoranges of the used satellites - pseudoranges[constellationSize]%% Inno: Kalman Filter innovation vector - gamma[constellationSize]%% CovInno: Covariance of the innovation vector S[constellationSize,constellationSize]%% EstimPos: Estimated position - x_meas[numStates]%% CovEstimPos: Covariance of estimated position P_meas[numStates,numStates]%% E: Error of the estimated position w.r.t the FINE location%% Fl, fineLocation.Latitude, fineLocation.Longitude, fineLocation.Altitude

Timestamp,151804.991
satID,E2_E5a,E3_E5a,E8_E5a
Elev,31.605128403307823,79.62832157886312,26.444315270751627
CN0,16.289081573486328,7.0,28.23590087890625
PR,2.5750934904783495E7,2.333896305167067E7,2.4267041365011785E7
Inno,-68.4693889580667,-14.273256249725819,56.99723841249943
CovInno,12.30689118032241,12.225310385509559,12.748825664173747
EstimPos,-1538794.0330434479,6188007.218084622,147517.95980434964,60.513005881266054,-0.07650599828580251
CovEstimPos,3.5650609146416046,11.651952822729175,0.10983037794901221,7.378173837050027,0.1659039590573222
Timestamp,151805.977
satID,E2_E5a,E3_E5a,E8_E5a
Elev,31.60238360235476,79.63143857156221,26.449246469580316
CN0,17.97366714477539,7.0,27.811687469482422
PR,2.5751162124187972E7,2.333894837615684E7,2.426659615772588E7
Inno,-67.31305849552155,26.573294311761856,59.239081893116236
CovInno,12.370071507207472,11.959563366594725,12.779523598584273
EstimPos,-1538796.767972425,6187998.305269976,147518.18976800854,64.50305773219014,0.15052998519679206
CovEstimPos,3.8782551550625617,10.76695595400356,0.10976875106489989,
7.323381205504991,0.1655885136785262

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 13

Sample Output Data from GNSS Compare: Galileo IF%% Timestamp: timestamp of the logged information%% satID: ID of the used satellited - satID[constellationSize]%% Elev: elevation of the used satellited%% CN0: signal strength of the used satellited%% PR: pseudoranges of the used satellites - pseudoranges[constellationSize]%% Inno: Kalman Filter innovation vector - gamma[constellationSize]%% CovInno: Covariance of the innovation vector S[constellationSize,constellationSize]%% EstimPos: Estimated position - x_meas[numStates]%% CovEstimPos: Covariance of estimated position P_meas[numStates,numStates]%% E: Error of the estimated position w.r.t the FINE location%% Fl, fineLocation.Latitude, fineLocation.Longitude, fineLocation.Altitude

Timestamp,151806.986
satID,E2_IF,E8_IF
Elev,31.600331705012515,26.45394837865013
CN0,20.635687828063965,21.006871700286865
PR,2.5751356947610945E7,2.4266128689324044E7
Inno,-36.301258727908134,23.8418588899076
CovInno,12.414912096895595,12.790598347487595
EstimPos,-1539135.6021293134,6187907.330222905,147761.15851517566,76.98333755833747,-1.2545498177828343
CovEstimPos,4.979483485938693,10.91566072767782,0.37545965944208776,7.
2979096935249945,0.17124149072750489
Timestamp,151807.982
satID,E2_IF,E8_IF
Elev,31.597603150492983,26.458894718625388
CN0,21.129629135131836,21.195557594299316
PR,2.5751580918449465E7,2.426566429794532E7
Inno,-37.94618817418814,7.6518113650381565
CovInno,12.627661334201402,12.921886508639407
EstimPos,-1539137.4731742432,6187905.216814774,147761.45753847074,85.06007273199538,-2.3242130689062286
CovEstimPos,5.321390125893703,11.175310803298517,0.3746353550231154,7.
242554375687705,0.17305188580421096
FL, 1.334548, 103.963698, 69.428651

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 14

Sample Output Data from GNSS Compare: GPS IF%% Timestamp: timestamp of the logged information%% satID: ID of the used satellited - satID[constellationSize]%% Elev: elevation of the used satellited%% CN0: signal strength of the used satellited%% PR: pseudoranges of the used satellites - pseudoranges[constellationSize]%% Inno: Kalman Filter innovation vector - gamma[constellationSize]%% CovInno: Covariance of the innovation vector S[constellationSize,constellationSize]%% EstimPos: Estimated position - x_meas[numStates]%% CovEstimPos: Covariance of estimated position P_meas[numStates,numStates]%% E: Error of the estimated position w.r.t the FINE location%% Fl, fineLocation.Latitude, fineLocation.Longitude, fineLocation.Altitude

Timestamp,151812.003
satID,G26_IF
Elev,22.16309675251404
CN0,27.370187759399414
PR,2.3462714691430964E7
Inno,26.649105709046125
CovInno,14.612919219946784
EstimPos,1539171.4916997314,6187946.377235158,147649.29725059884,173.36701186
45205,5.017057874788763
CovEstimPos,42.02897126567105,107.28515377289504,0.44981468969140076,
21.18905070597317,0.19791212585694115
Timestamp,151813.000
satID,G26_IF
Elev,22.15758607213537
CN0,27.257532119750977
PR,2.3463242909665097E7
Inno,14.858850326389074
CovInno,14.613104364130892
EstimPos,1539171.4908786018,6187946.3789502,147649.2972503797,182.83431188868
224,5.749231914786899
CovEstimPos,42.42897122104428,107.68515357821494,0.4498146896913976,2
1.266921424507053,0.1979121601578303

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 15

Sample Output Data from GNSS Compare: GPS L5%% Timestamp: timestamp of the logged information%% satID: ID of the used satellited satID[constellationSize]%% Elev: elevation of the used satellited%% CN0: signal strength of the used satellited%% PR: pseudoranges of the used satellites pseudoranges[constellationSize]%% Inno: Kalman Filter innovation vector gamma[constellationSize]%% CovInno: Covariance of the innovation vector S[constellationSize,constellationSize]%% EstimPos: Estimated position - x_meas[numStates]%% CovEstimPos: Covariance of estimated position P_meas[numStates,numStates]%% E: Error of the estimated position w.r.t the FINE
location%% Fl, fineLocation.Latitude, fineLocation.Longitude,fineLocation.Altitude

Timestamp,151809.994
satID,G3_L5,G8_L5,G26_L5,G27_L5
Elev,33.554008258553964,48.25266196872872,22.172884345386976,69.17191391228158
CN0,7.0,7.587594985961914,27.545879364013672,7.0
PR,2.2486976274135992E7,2.1399447049904224E7,2.3461578159287207E7,2.037386396972175
E7
Inno,-11.486417010426521,-185.52537051960826,11.411866538226604,-3.4237166941165924
CovInno,13.059042573981515,11.44264234398441,12.562314719364762,11.488008770518793
EstimPos,-1538709.1848842017,6187818.624650529,147586.2714535496,-179.64025496705435,3.23141724321601
CovEstimPos,1.9450529003611876,4.055592318489455,0.028066350060091515,2.698096149298
294,0.14778180419855916
Timestamp,151811.004
satID,G3_L5,G8_L5,G26_L5,G27_L5
Elev,33.555140950600425,48.26073313267819,22.16732824108486,69.17718627177668
CN0,7.0,7.698572158813477,27.744571685791016,8.866910934448242
PR,2.2486897137517832E7,2.1399212013040777E7,2.3462105801640287E7,2.037368739108773
E7
Inno,19.807740181684494,98.11360029876232,30.837399903684855,22.741700060665607
CovInno,13.031395039905892,11.432938504587097,12.659255609347586,11.494742118975221
EstimPos,-1538708.1414774146,6187814.753371424,147586.37595550265,166.99368733602827,0.5669135448602654
CovEstimPos,1.9732213736391175,4.107242201226502,0.02802445735602994,2.7648003911385
923,0.14775082770243753

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 16

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 17

Android Raw Data Logging APP: RTKDROID• External GNSS Receiver can beconnected to Android Device• Base-Station is connected via NTRIP
Address• VRS Correction also supported• Supported File Format• ubx (u-blox)• Other formats will be included if
requested• SBF (Septentrio) will be included innear future• Real-Time RTK• Raw Data can be logged for PostProcessing• Output from RTKDROID can be send
to other APKs in the device
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 18

Android APP to Input GNSS Data for GIS: SW Maps
• Excellent APP to collect GIS Data in
the field• Internal or External GNSS Receivercan be used
• External Receiver can be connected
via BT or USB Cable• Many Popular File Formats are
Supported• u-blox• Topcon• Trimble• Septenetrio• Garmin• Or Any Receiver with NMEA output• Output from RTKDROID can be send
to SW Maps

RTKDROID and SW MAPS run in many Android Devices that has OS 5.0 or later
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 19

Android Devices Capable to Output GNSS Raw Data

See https://developer.android.com/guide/topics/sensors/gnss for detail list of compatible devices
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 20

GNSS Raw Data Analysis Tool for GnssLogger• GNSS Analysis APP• Matlab-based Tool
• Linux, Windows, MacOS• Version 2.6.3.0• Release Notes:
https://developer.android.com/guide/topics/sensors/g
nss#releaseGNSS Analysis app v2.6.3.0 release notes.

The GNSS Analysis app is built on MATLAB, but you don't need to have MATLAB to run
it. The app is compiled into an executable that installs a copy of the MATLAB Runtime.
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 21

Raw Measurement : Lecture Notes by Dr. Frank van Diggelen, Google Inc.

Download the Lecture Notes from https://home.csis.u-tokyo.ac.jp/~dinesh/GNSS_Raw.htm
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 22

GNSS Raw Data Measurement Task Force Members

CSIS, The University of Tokyo is also a Member of the Task Forcehttps://www.gsa.europa.eu/gnss-raw-measurements-task-force
Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 23

GNSS Raw Data Measurement Task Force Members
Beuth University

Industrie- und Hnadelskammer
Reutlingen, Institut für
Wissensmanagement undWissenstransfer (IHK-IWW)

Blue Dot SolutionsENAC

BNB-Consulting

Ericsson Research

CGEOS - Creative Geosensing Sprl-s

European Space Agency (ESA)

Circular Devices

ESSP SAS

Airbus Defence & Space
Astri Polska Sp. z o. o.

European GNSS Agency (GSA)
Feyman Technology Inc
Finnish Geospatial Research Institute(FGI), Department of Navigation and
Crowdloc
Positioning National Land Survey of
CS Systemes d`Information
FinlandCentre Tecnològic Telecomunicacions FlowscapeCatalunya (CTTC)GCE
DDK Positioning Ltd
GeneGIS GI s.r.l.
DEIMOS Space S.L.U.
Geneq Inc
Geo++ GmbH
Geodetic Observatory Pecny
GEOFLEX
CNES
Consejo Superior de Investigaciones
Científicas

GeoNumerics S.L.
Global Infotech Corporation
Hochschule Karlsruhe (HSKA) University of Applied Sciences
Institute of Environmental Sciences
and Technology (IEST)IFSTTAR
Ineco
Infinite Dimensions Integration
GmbH
Istituto Nazionale di Geofisica e
Vulcanologia
Joint Research Centre, European
Commission
Jomo Kenyatta University
K.N. Toosi University of Technology
Kingston University
Magneti Marelli
NAVBLUE
Nottingham Scientific Ltd

Ordnance Survey
Politecnico di Torino
RHEA GroupRISE Research Institutes of
Sweden AB
Rockwell Collins UK
RokubunRx Networks Inc.
SAT4M2M
Septentrio
Sogei S.p.A.
Space Kinetics
SpaceEXE s.r.l
Spirent Communications plc
STMicroelectronics
Swift Navigation

Syntony+A77A46A6A61:A75
Ordnance Survey+A61:A75
Technology Centre CAS Prague
TeleConsult Austria GmbH
Telespazio France
Telespazio Ibérica
Thales Services
International University of La Rioja(UNIR)
Universität der Bundeswehr, München
College & University Hrvatsko Zagorje
Krapina
University of Appl. Sciences Karlsruhe,
GNSS & Navigation Laboratory
University of Calgary
University of Coimbra
University of Liege
University of Nottingham

University of Rijeka
University of Rome "La Sapienza" - Geodesy and Geomatics Division

University of Tokyo, Center for Spatial Information Science
University of Warmia and Mazury in Olsztyn
University of Zagreb
The United Nations Office for Outer Space Affairs (UNOOSA)
wawa
Wrocław University of Environmental and Life Sciences

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 24

White Paper on GNSS Raw Data Measurement

http://galileognss.eu/wpcontent/uploads/2018/05/Using-GNSSRaw-Measurements-on-Androiddevices.pdf

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 25

GNSS Raw Data Measurement fromAndroid Devices
Tutorial Note by Dr. Frank, Google Inc.

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 26

User Interface of GNSS Analysis
Raw Data Processing Tool

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 27

Test Data Site, Carlston Garden, Melbourne, Australia

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 28

Test Data Site, Carlston Garden, Melbourne, Australia

Location of Android
Smart-Phone, on the bench

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

Slide : 29

Dinesh Manandhar, CSIS, The University of Tokyo, dinesh@iis.u-tokyo.ac.jp

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