Ionosphere-Constrained Single-Frequency PPP with an Android Smartphone and Assessment of GNSS Observations

sensors

Article Ionosphere-Constrained Single-Frequency PPP with an Android Smartphone and Assessment of GNSS Observations

Guangxing Wang 1, Yadong Bo 1, Qiang Yu 1, Min Li 2, Zhihao Yin 1 and Yu Chen 1,* 1 School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China; [email protected] (G.W.); [email protected] (Y.B.); [email protected] (Q.Y.); [email protected] (Z.Y.) 2 GNSS Research Center, Wuhan University, Wuhan 430079, China; [email protected] * Correspondence: [email protected]

Received: 31 August 2020; Accepted: 16 October 2020; Published: 20 October 2020

Abstract: With the development of Global Navigation Satellite System (GNSS) and the opening of Application Programming Interface (API) of Android terminals, the positioning research of Android terminals has attracted the attention of GNSS community. In this paper, three static experiments were conducted to analyze the raw GNSS observations quality and positioning performances of the smartphones. For the two experimental smartphones, the numbers of visible satellites with dual-frequency signals are unstable and not enough for dual-frequency Precise Point Positioning (PPP) processing all through the day. Therefore, the ionosphere-constrained single-frequency PPP model was employed to improve the positioning with the smartphones, and its performance was evaluated and compared with those of the Single Point Positioning (SPP) and the traditional PPP models. The results show that horizontal positioning accuracies of the smartphones with the improved PPP model are better than 1 m, while those with the SPP and the traditional PPP models are about 2 m.

Keywords: Android smartphones; GNSS raw observation; Precise Point Positioning (PPP); multipath; BDS

1. Introduction With the development of Global Navigation Satellite System (GNSS) in the 21th century, the number of available navigation satellites has increased signiﬁcantly. It has formed a multi-system global navigation constellation framework mainly composed of American Global Positioning System (GPS), Russian Global Navigation Satellite System (GLONASS), Chinese BeiDou Navigation Satellite System (BDS) and EU’s Galileo system [1]. Moreover, Japanese Quasi-Zenith Satellite System (QZSS) is widely used to supplement GPS services as a regional navigation satellite system. The development of GNSS also promotes the continuous development of location-based service business based on smartphones, and greatly facilitates both industrial production and daily life. For a long time, only rough positioning results could be obtained from the smartphones. Launched in 2016, the Android 7.0 operating system supports the access to GNSS raw measurements and navigation messages [2], making the Android devices function more like a GNSS receiver. In the same year, Google released GNSSLogger, an open-source program that could help retrieve GNSS raw observations from Android smartphones, including the observations of code pseudorange, carrier phase, and Doppler [3]. In 2017, Geo++ released an application named Geo++ RINEX Logger to provide GNSS raw observation directly in RINEX format [4]. Both of the above two applications cannot output the information of navigation messages. RinexON, released by Flamingo team at the end of June 2018, is able to output multi-GNSS raw data, including broadcast ephemeris, collected

Sensors 2020, 20, 5917; doi:10.3390/s20205917 www.mdpi.com/journal/sensors Sensors 2020, 20, 5917 2 of 15 by smartphones in RINEX 3.0.3 format [5]. The accessibility of GNSS raw data makes it possible to analyze the observation quality and to study the positioning algorithm with Android terminals. In recent years, GNSS positioning with Android terminals has become one of research focuses. The experiment by Gim et al. [6] is a Single Point Positioning (SPP) test with code measurements of the Nexus 9 tablet, and the results show that the RMS of positioning errors in horizontal and three-dimensional (3D) are 3.05 m and 3.82 m. In an experiment of double-differenced positioning with single-frequency carrier phases from the tablet and several base stations, the positioning accuracy better than 20 cm can be achieved within 20 min [7]. The carrier-to-noise ratio (C/N0) value of GNSS raw observations collected by the Nexus 9 tablet is 10 dBHz lower than the representative values obtained from a geodetic-quality antenna and receiver. With time-differenced filtering method, horizontal and vertical accuracies of static positioning can be better than 0.6 and 1.4 m, respectively [8]. Martin [9] also used a Nexus 9 tablet for positioning test. The research shows that multipath plays an important role for the expected accuracy of the calculated precise positions, both due to the induced error on the measurements, and due to loss of lock of the GNSS signals, which significantly affects precise positioning from carrier phase measurements. Although these studies have important implications for subsequent experiments using smartphones, the positioning of ordinary smartphones is not comparable to this tablet. Many scholars have carried out differential GNSS researches on smartphones. Zhang et al. [10] developed an Android application based on wide area differential location technology, and the static observation results show that the horizontal accuracy of the smartphones can reach about 4 m. The maritime test by Specht et al. [11] showed that the accuracy of the dynamic positioning with smartphones during vessel maneuvering can reach 10 m, satisfying most of the maritime requirements for navigation accuracy. The Network Real Time Kinematic (NRTK) positioning accuracy with smartphones by Dabove et al. [12] is about 60 cm. Moreover, Wanninger et al. [13] performed carrier phase ambiguity fixing for smartphones. With ambiguities successfully fixed, the 3D positioning accuracies (standard deviations) better than 4 cm could be achieved after five minutes of static observation session, and an accuracy of 2 cm is possible for long observation sessions. Since the launch of Xiaomi 8 in June 2018 [14], smartphones supporting dual-frequency GPS signals have become the mainstream of the market and motivate the research of smartphones Precise Point Positioning (PPP). The quality analysis results of GNSS raw observations show that the number of visible satellites with Xiaomi 8 is similar to that with geodetic receivers, although the carrier-to-noise ratio and multipath effect with smartphones are worse than the typical values with geodetic receivers [15]. It is found that carrier phase measurements collected by smartphones might contain gross errors and systematic errors, and that different clocks are used for code and carrier phase observations [16]. By considering the clock bias between the code and carrier phase measurements, the accuracy of PPP can be better than 1 m [17]. Shi et al. [18] conducted static and dynamic observation experiments with Samsung S8, Huawei Mate20 and Xiaomi8. After experiments, he evaluated the GNSS data quality of smartphones in detail. Through proper GNSS data quality control, he initially achieved positioning accuracy within 1 m. Wu et al. [19] conducted long-term static observation with smartphones. The PPP results show that the positioning accuracy of smartphone with dual frequency data is better than 20 cm, but it takes up to 100 min to converge. These studies confirm the practicability of using GNSS raw data of smartphones to realize PPP, which is of great significance for subsequent PPP studies using smartphones. According to the above published researches, the difficulties of precise positioning with smartphones were found. Compared with geodetic receivers, smartphones are prone to suffer from frequent losses of lock, unstable clock and poor quality of measurements, due to the relatively low-cost GNSS chips and antennas [15–18]. Therefore, the GNSS raw observations collected by smartphones are likely to contain a large number of cycle slips, and to be seriously affected by multipath effects and low carrier-to-noise ratio, jeopardizing the PPP accuracies with smartphones. To overcome the Sensors 2020, 20, x FOR PEER REVIEW 3 of 15

To overcome the disadvantages of smartphones in GNSS data collection, it is necessary to Sensorsquantitatively2020, 20, 5917 analyze the quality of observations of smartphones and to study the method of 3cycle of 15 slip detection. Although some types of smartphones are designed with the nominal capabilities of tracking disadvantages of smartphones in GNSS data collection, it is necessary to quantitatively analyze the dual-frequency GNSS signals, the real measurements are usually far from enough for quality of observations of smartphones and to study the method of cycle slip detection. dual-frequency PPP processing all through the day with the traditional method, due to various Although some types of smartphones are designed with the nominal capabilities of tracking reasons such as the frequent interruptions of dual-frequency data. Thus, the single-frequency PPP is dual-frequency GNSS signals, the real measurements are usually far from enough for dual-frequency still a common processing mode for smartphones, and the ionospheric delay are corrected by the PPP processing all through the day with the traditional method, due to various reasons such as the Global Ionospheric Maps (GIM), the products of which can correct about 80% of the ionospheric frequent interruptions of dual-frequency data. Thus, the single-frequency PPP is still a common delay [20]. Although the remaining ionospheric delay can still reach decimeter level, it is sufficient processing mode for smartphones, and the ionospheric delay are corrected by the Global Ionospheric for the single-frequency PPP with smartphones, considering that the precision of code pseudorange Maps (GIM), the products of which can correct about 80% of the ionospheric delay [20]. Although measurements from smartphones is nearly 10 m [18]. In theory, GIM can be used to build constraint the remaining ionospheric delay can still reach decimeter level, it is sufficient for the single-frequency equations, to improve the reliability of single-frequency PPP processing of smartphones and shorten PPP with smartphones, considering that the precision of code pseudorange measurements from the convergence time [21]. However, there is a lack of research on the impact of ionosphere smartphones is nearly 10 m [18]. In theory, GIM can be used to build constraint equations, to improve constraints on the smartphones PPP. the reliability of single-frequency PPP processing of smartphones and shorten the convergence time [21]. This paper contributes to GNSS data quality analysis and PPP with Android smartphones. However, there is a lack of research on the impact of ionosphere constraints on the smartphones PPP. Section 2 represents the principles and methodologies of single-frequency PPP, smoothing code This paper contributes to GNSS data quality analysis and PPP with Android smartphones. pseudorange and cycle slip detection, in special consideration of the characteristics of data collected Section2 represents the principles and methodologies of single-frequency PPP, smoothing code by smartphones. In Section 3, the quality of GNSS measurements from two different Android pseudorange and cycle slip detection, in special consideration of the characteristics of data collected by devices and one geodetic receiver are analyzed and compared from the aspects of satellite tracking smartphones. In Section3, the quality of GNSS measurements from two di fferent Android devices and performance, carrier-to-noise ratio and multipath effects, followed by PPP experiment with GNSS one geodetic receiver are analyzed and compared from the aspects of satellite tracking performance, Analysis Software for Multi-constellation and Multi-frequency Precise Positioning (GAMP) [22]. carrier-to-noise ratio and multipath effects, followed by PPP experiment with GNSS Analysis Software Conclusions are given in Section 5. for Multi-constellation and Multi-frequency Precise Positioning (GAMP) [22]. Conclusions are given in Section5. 2. The Principles and Methodologies of PPP with Smartphones 2. TheAndroid Principles 7.0 andprovides Methodologies positioning of PPPrelated with AP SmartphonesI. The modules related to the raw GNSS observationsAndroid 7.0are providesGNSS Clock, positioning GNSS relatedMeasurement API. The and modules GNSS related Navigation to the rawMessage. GNSS observationsThe module areGNSS GNSS Clock Clock, provides GNSS quartz Measurement clock information and GNSS of Navigation the Android Message. smartphones. The module The module GNSS GNSS Clock providesMeasurement quartz provides clock information observation of information the Android of smartphones.each satellite signal The module obtained GNSS by base Measurement frequency providesprocessing. observation The module information GNSS Navigation of each satelliteMessage signal provides obtained satellite by ephemeris base frequency information. processing. The Themodules module GNSS GNSS Clock Navigation and GNSS Message Measurement provides satellitecan generate ephemeris the raw information. GNSS observations The modules of GNSS each Clocksatellite, and including GNSS Measurement code pseudorange, can generate carrier the rawphase, GNSS Doppler observations and carrier-to-noise of each satellite, ratio. including The codegeneration pseudorange, principle carrier of GNSS phase, raw Doppler observations and carrier-to-noise through the ratio. modules The generation GNSS Clock principle and of GNSS rawMeasurement observations is shown through in theFigure modules 1 [18]. GNSS Clock and GNSS Measurement is shown in Figure1[ 18].

Figure 1. Generation principle of GNSS raw observations. Figure 1. Generation principle of GNSS raw observations.

Sensors 2020, 20, 5917 4 of 15

2.1. Ionosphere-Constrained Single-Frequency PPP Since the numbers of dual-frequency observables tracked by the current smartphones are far from sufficient, the single-frequency PPP model is still the main choice for the positioning research with smartphones at present [17]. Without dual-frequency observables, the ionospheric delay is unable to be reduced through the ionosphere-free combination, and has to be estimated as an additional unknown parameter. Besides, the qualities of code and carrier phase measurements received by smartphone are usually not as high as those by geodetic receiver [18]. To improve the PPP accuracy with smartphone, external ionospheric information is introduced as virtual observations. The model of single-frequency PPP with ionospheric constraint for one satellite-receiver pair can be expressed as.    x         p u 1 M 1 0  δt  ε                ϕ  =  u 1 M 1 λ  Zw  +  ξ , Qp, Qϕ, QI (1)    −      eI   0 0 0 1 0  I   ζ     N  where p and ϕ represent the observed-minus-computed (OMC) values of the code pseudorange and the carrier phase, respectively; eI denotes the virtual observable of the ionospheric constraint calculated by the GIM products; u is the line-of-sight direction vector specific to each satellite; x is the incremental vector of the receiver position with respect to the initial approximate coordinates; δt and Zw are the parameters of receiver clock and Zenith Wet Delay(ZWD) specific to the station; M and λ are the mapping function of ZWD and the signal wavelength; I and N are the ionospheric delay and carrier phase ambiguity specific to certain station, satellite and frequency; ε, ξ and ζ are the sums of unmodeled errors and observing noise corresponding to each observable; Qp, Qϕ and QI represent the variances of the corresponding observables. It is worth noting that all the indices of satellite, receiver and frequency have been omitted for simplicity. In this study, the rapid products of orbits and clocks released by the Multi-GNSS Experiment (MGEX) are adopted, and the satellite Differential Code Biases (DCBs) are corrected by the DCB products from the Center for Orbit Determination in Europe (CODE) and the MGEX. The satellite antenna phase center offsets and variations, relativistic effects, zenith dry delay, tidal loadings, and phase windup are corrected by the empirical models [23].

2.2. Smoothing Code Pseudorange with Doppler Although the method of phase smoothing pseudorange is usually used to reduce the noise of code measurements, its performance is prone to be affected by the continuity of phase observation, especially in the cases of poor tracking conditions with smartphones. Compared with the phase observation, the Doppler observation is unlikely to be affected by cycle slips, and its accuracy is higher than that of the code measurement. Therefore, the Doppler observation can be used alternatively to reduce the noise and multipath error of the code measurement [24]. The integral Doppler is equal to the variation of carrier phase during the integral interval, which reflects the variation of geometric distance between the satellite and the smartphone. The algorithm of Doppler smoothing code pseudorange can be expressed as Z ! 1 m 1 ti Pti = Pti + − Pti 1 + λ Ddt (2) m m − · ti 1 − where ti and ti 1 are two adjacent moments; P and P represent the raw and smoothing code pseudorange; − D is the Doppler observation; m is a constant set as 75 in the study. Sensors 2020, 20, 5917 5 of 15

2.3. Cycle Slip Detection Due to the lack of dual-frequency GNSS observations, cycle slip detection is another challenging taskSensors for 2020 the, 20 smartphones., x FOR PEER REVIEW At present, the main cycle slip methods for single-frequency scenario include5 of 15 code-phase comparison, Doppler integration, and higher-order time differencing of carrier phase observationsDoppler integration [25–27]. are In thisemployed study, thefor the methods detection of code-phase of cycle slips comparison contained and in the Doppler data collected integration by arethe employedsmartphones. for the detection of cycle slips contained in the data collected by the smartphones. InIn thethe observation observation equation, equation, the the terms terms of of geometric geometric distance, distance, receiver receiver clock clock and and satellite satellite clock clock can becan eliminated be eliminated through through the differencing the differencing between the between raw measurements the raw ofmeasurements the code pseudorange of the Pcodeand thepseudorange carrier phase 𝑃 Φ and, so the we carrier have phase 𝛷, so we have ∆ = P Φ = 2I λN + ε ξ (3) 𝛥=𝑃−𝛷=2𝐼−𝜆𝑁+𝜀−𝜉− − − (3) wherewhere thethe parametersparameters havehave thethe same same meaning meaning as as Equation Equation (1). (1). IfIf nono cyclecycle slipslip occurs,occurs, thethe ambiguityambiguity parameterparameter staysstays constant.constant. TheThe ionosphericionospheric delaydelay variesvaries slowly,slowly, andand soso isis thethe code-phasecode-phase didifferencingfferencing ∆𝛥.. TheThe temporaltemporal variationvariation of ∆𝛥 staysstays relativelyrelatively stablestable unlessunless therethere are are cycle cycle slips. slips. As As a a consequence, consequence, the the between-epoch between-epoch di ffdifferencingerencing of Equationof Equation (3) can(3) can be usedbe used to detect to detect and and determine determine the the possible possible cycle cycle slips. slips. TheThe abovementionedabovementioned cycle cycle slip slip detection detection method method is highlyis highly dependent dependent on theon qualitythe quality of the of code the measurements.code measurements. To obtain To the obtain knowledge the aboutknowledge the precisions about andthe stabilitiesprecisions of theand measurements stabilities of with the dimeasurementsfferent devices, with we investigatedifferent devices, the high-order we investig between-epochate the high-order differences between-epoch of raw measurements, differences and of shownraw measurements, in Figure2. are and the shown typical in third-order Figure 2. are between-epoch the typical third-order di fferences between-epoch of the code (blue) differences and the of carrierthe code phase (blue) (orange) and measurements,the carrier phase as well (orange) as the second-ordermeasurements, between-epoch as well as ditheff erencessecond-order of the Dopplerbetween-epoch (red) measurements. differences of the Doppler (red) measurements.

Figure 2. The third-order between-epoch differences of code (blue) and carrier phase (orange) measurements, Figure 2. The third-order between-epoch differences of code (blue) and carrier phase(orange) as well as the second-order between-epoch differences of the Doppler (red) measurements on L1 measurements, as well as the second-order between-epoch differences of the Doppler (red) frequency of G26 satellite with the Mate30 (a), the V20 (b) and the Trimble R8 (c). The numbers measurements on L1 frequency of G26 satellite with the Mate30 (a), the V20 (b) and the Trimble R8 with different colors listed at the bottom right corner of each panel represent the corresponding (c). The numbers with different colors listed at the bottom right corner of each panel represent the standard deviations. corresponding standard deviations. The code measurements of the two smartphones are far less precise than the those of the geodetic receiver,The whichcode measurements is likely to undermine of the two the performancesmartphones of are the far cycle less slip precise detection than method the those based of the on Equationgeodetic (3).receiver, However, which the is Doppler likely to measurements undermine the of performance all the three devices of the cycle are comparable slip detection in precision method andbased stability, on Equation suggesting (3). However, that the Doppler the Doppler measurements measurements can be of used all the as three supplements devices ofare the comparable code and phasein precision measurements and stability, in the suggesting detection ofthat cycle the slips.Doppler measurements can be used as supplements of the codeTherefore, and phase an indicator measurements based on in the the di detectionfference between of cycle theslips. carrier phase and Doppler integral are calculatedTherefore, as [27 an] indicator based on the difference between the carrier phase and Doppler integral are calculated as [27] R ti ∆Φ(ti 1, ti) Ddt − − ti 1 δN = − (4) ∆𝛷(𝑡,𝑡λ)− 𝐷 d𝑡 𝛿𝑁 = (4) where ∆Φ(ti, ti 1) is the variation of the carrier phase between𝜆 two adjacent epochs. An absolute value − ofwhereδN larger ∆𝛷(𝑡 than,𝑡) the is thresholdthe variation indicates of the the carrier cycle phase slip. between two adjacent epochs. An absolute value of 𝛿𝑁 larger than the threshold indicates the cycle slip. The methods of code-phase comparison based on Equation (3) and Doppler integration based on Equation (4) are used together to guarantee the performance of cycle slip detection. In summary, the flowchart of ionosphere-constrained single-frequency PPP for smartphone is shown as Figure 3.

Sensors 2020, 20, 5917 6 of 15

The methods of code-phase comparison based on Equation (3) and Doppler integration based on Equation (4) are used together to guarantee the performance of cycle slip detection. In summary, theSensors ﬂowchart 2020, 20, x of FOR ionosphere-constrained PEER REVIEW single-frequency PPP for smartphone is shown as Figure6 of3 .15

Figure 3. Flowchart of ionosphere-constrained single-frequency PPP processing for the smartphones. Figure 3. Flowchart of ionosphere-constrained single-frequency PPP processing for the smartphones. 3. GNSS Data Quality Analysis 3. GNSS Data Quality Analysis The main device used in this experiment is a Huawei Mate30 smartphone (hereinafter referred to asThe Mate30). main Fordevice comparison used in this analysis, experiment a Huawei is a Huawei honorV20 Mate30 smartphone smartphone (hereinafter (hereinafter referred referred to as V20)to as andMate30). a geodetic For comparison receiver (Trimble analysis, R8) a were Huawei also used.honorV20 The Mate30smartphone smartphone (hereinafter is a dual-frequency referred to as GNSSV20) and smartphone a geodetic that collectsreceiver the (Trimble ﬁrst frequency R8) were signals also of GPS,used. GLONASS, The Mate30 BDS, Galileosmartphone and QZSS,is a anddual-frequency the second frequencyGNSS smartphone signals of that GPS, collects Galileo the and first QZSS. frequency Although signals the V20 of GPS, smartphone GLONASS, is cheaper BDS, thanGalileo the and Mate30 QZSS, smartphone, and the second it also frequency supports dual-frequency signals of GPS, observations Galileo and andQZSS. all ofAlthough the ﬁve systems.the V20 Listedsmartphone in Table is 1cheaper are the GNSSthan the related Mate30 characteristics smartphone of, theit also three supports devices dual-frequency used in this paper. observations and allIn of the the experiment, five systems. Trimble Listed R8 in isTable used 1 are as thethe reference,GNSS related and characteristics its antenna is of only the three about devices 10 cm awayused in from this thepaper. two smartphones. To compare the observing positioning performances of the two smartphones with those of the geodetic receiver, synchronous observations were conducted with the three devices. Data was collected on the evening of 13 November, the afternoon and the evening of 18 November 2019, and each of the three observing periods lasted for 2–3 h. Since the devices

Sensors 2020, 20, x FOR PEER REVIEW 7 of 15

Table 1. Performance features of the Mate30, the V20 smartphones and Trimble R8 geodetic receiver.

Devices Android Version GNSS Supported 1 Code Carrier Phase G (L1+L5), R (G1), Mate30 10 Yes Yes Sensors 2020, 20, 5917 E (E1+E5A), C (B1), J (L1+L5) 7 of 15 G (L1+L5), R (G1), V20 10 Yes Yes E (E1+E5A), C (B1), J (L1+L5) were equipped at almost the same place during the three observing periods, the diﬀerence of the G (L1+L2), R (G1+G2), surroundingsTrimble R8 could be neglected. \ Yes Yes E (E1+E5A), C (B1+B2), J (L1+L2) 1 Table G: GPS, 1. Performance R: GLONASS, features E: Galileo, of the C: Mate30,BDS, J: QZSS. the V20 The smartphones definition of and freque Trimblency bands R8 geodetic is described receiver. in RINEX 3.03 format [28]. Devices Android Version GNSS Supported 1 Code Carrier Phase In the experiment, Trimble R8 is used as the reference, and its antenna is only about 10 cm G (L1+L5), R (G1), away fromMate30 the two smartphones. 10 To compare the observing positioningYes performances Yes of the two E (E1+E5A), C (B1), J (L1+L5) smartphones with those of the geodetic receiverG (L1, synchronous+L5), R (G1), observations were conducted with V20 10 Yes Yes the three devices. Data was collected on theE(E1 evening+E5A), of C (B1),13 November, J (L1+L5) the afternoon and the evening G (L1+L2), R (G1+G2), of 18 NovemberTrimble R8 2019, and each of the three observing periods lasted forYes 2–3 h. Since Yes the devices were equipped at almost the\ same placeE (E1during+E5A), the C (B1three+B2), observing J (L1+L2) periods, the difference of the 1 surroundingsG: GPS, R: could GLONASS, be neglected. E: Galileo, C: BDS, J: QZSS. The deﬁnition of frequency bands is described in RINEX 3.03 format [28].

3.1. Satellite Tracking 3.1. Satellite Tracking Shown in Figure 4 are the numbers of different GNSS satellites with the signal of the first Shown in Figure4 are the numbers of di fferent GNSS satellites with the signal of the first frequency frequency tracked by the geodetic receiver and the two smartphones. The Mate30 smartphone is tracked by the geodetic receiver and the two smartphones. The Mate30 smartphone is able to track able to track about 38 satellites in total, while the V20 smartphone tracks only 30 ones. Since the about 38 satellites in total, while the V20 smartphone tracks only 30 ones. Since the GNSS chip of the GNSS chip of the Mate30 is nominally supporting BDS-3 signals, it can track more BDS satellites Mate30 is nominally supporting BDS-3 signals, it can track more BDS satellites than the V20, which can than the V20, which can only track BDS-2 satellites. Compared with the geodetic receiver, both of only track BDS-2 satellites. Compared with the geodetic receiver, both of the two smartphones suffer the two smartphones suffer from frequent fluctuations in the numbers of visible satellites, although from frequent fluctuations in the numbers of visible satellites, although they can track as many satellites they can track as many satellites as, if not more than, the geodetic receivers. as, if not more than, the geodetic receivers.

Figure 4.4. TheThe numbers numbers of of GPS GPS (blue), (blue), BDS BDS (red), (red), GLONASS GLONASS (green), (green), Galileo Galileo (cyan) and(cyan) QZSS and (orange) QZSS (orange)satellites satellites with the with signal the of signal the ﬁrst of frequencythe first frequency tracked bytracked the Mate30 by the smartphoneMate30 smartphone (a,d,g), the(a,d V20,g), smartphone (b,e,h) and the geodetic receiver Trimble R8 (c,f,i) during the ﬁrst (a–c), the second (d–f) and the third (g–i) observing periods.

the V20 smartphone (b,e,h) and the geodetic receiver Trimble R8 (c,f,i) during the first (a–c), the second (d–f) and the third (g–i) observing periods.

Shown in Figure 5 are the numbers of satellites with dual-frequency signals tracked by the Mate30, the V20 and the Trimble R8. The number of satellites with dual-frequency signals tracked by the Mate30 smartphone fluctuates between 3 and 10, while the number of satellites with dual-frequency signals tracked by the V20 smartphone fluctuates between 1 and 9. The dual-frequency measurements collected by either the Mate30 or the V20 are far from sufficient for continuous PPP experiment with the ionosphere-free combination of L1/L5 or E1/E5A observables, and the case might be worse in real scenarios with urban canyons. The data continuities of GPS, Galileo and QZSS are also compared and the results are shown in SensorsFigure2020 5., 20The, 5917 number of GPS, Galileo and QZSS dual-frequency satellites of two smartphones8 of 15 fluctuates seriously. And in a certain period of time, the dual-frequency satellite of Galileo cannot be signalsobserved tracked by the by two the V20smartphones. smartphone Considering ﬂuctuates betweenthat QZSS 1 and system 9. The is dual-frequencya regional navigation measurements satellite collectedamplification by either system the developed Mate30 or by the Japan, V20 are adopting far from the su ﬃInclinedcient for Geosynchronous continuous PPP Orbit experiment (IGEO), with the theprecision ionosphere-free of positioning combination service of provided L1/L5 or E1is /limitedE5A observables, [22]. Therefore, and the the case reliable might bedual-frequency worse in real scenariosmeasurements with urbancollected canyons. by the smartphones are still mainly from GPS satellites at present.

FigureFigure 5.5. TheThe numbersnumbers ofof GPSGPS (blue),(blue), GalileoGalileo (cyan),(cyan), QZSSQZSS (orange)(orange) andand thethe totaltotal satellitessatellites ofof thesethese threethree systems systems (red) (red) with with dual-frequency dual-frequency measurements measurements available availabl fore thefor Mate30the Mate30 smartphone smartphone (a,d, g(a), andd,g) theand V20 the smartphone V20 smartphone (b,e,h )(b and,e,h the) and geodetic the geodetic receiver receiver Trimble Trimble R8 (c,f, iR8) during (c,f,i) theduring ﬁrst the (a– cfirst), the (a second–c), the (secondd–f) and (d the–f) thirdand the (g– thirdi) observing (g–i) observing periods. periods.

3.2. Carrier-to-NoiseThe data continuities Ratio of GPS, Galileo and QZSS are also compared and the results are shown in Figure5. The number of GPS, Galileo and QZSS dual-frequency satellites of two smartphones fluctuates The carrier-to-noise ratio refers to the ratio of the average power of the carrier signal received at seriously. And in a certain period of time, the dual-frequency satellite of Galileo cannot be observed by the receiver end to the average power of the noise when the signal is interfered in the process of the two smartphones. Considering that QZSS system is a regional navigation satellite amplification propagation. The carrier-to-noise ratio reflects the noise level of the measurement [28]. The higher system developed by Japan, adopting the Inclined Geosynchronous Orbit (IGSO), the precision of the carrier-to-noise ratio is, the better the observation quality is. positioning service provided is limited [22]. Therefore, the reliable dual-frequency measurements Shown in Figure 6 are the mean values of carrier-to-noise ratio in three experiments. Generally, collected by the smartphones are still mainly from GPS satellites at present. the mean carrier-to-noise ratio with the geodetic receiver is the highest among all of the three 3.2.devices, Carrier-to-Noise although Ratiofor several BDS satellites the mean carrier-to-noise ratio with the Mate30 The carrier-to-noise ratio refers to the ratio of the average power of the carrier signal received at the receiver end to the average power of the noise when the signal is interfered in the process of propagation. The carrier-to-noise ratio reflects the noise level of the measurement [28]. The higher the carrier-to-noise ratio is, the better the observation quality is. Shown in Figure6 are the mean values of carrier-to-noise ratio in three experiments. Generally, the mean carrier-to-noise ratio with the geodetic receiver is the highest among all of the three devices, although for several BDS satellites the mean carrier-to-noise ratio with the Mate30 smartphone is the highest. The carrier-to-noise ratios with the Mate30 smartphone are around 40 dBHz, obviously better than those with the V20 smartphone. This may be due to better antenna and GNSS chip of the Mate30 smartphone. Sensors 2020, 20, x FOR PEER REVIEW 9 of 15 Sensors 2020, 20, x FOR PEER REVIEW 9 of 15 smartphone is the highest. The carrier-to-noise ratios with the Mate30 smartphone are around 40 smartphone is the highest. The carrier-to-noise ratios with the Mate30 smartphone are around 40 SensorsdBHz,2020 obviously, 20, 5917 better than those with the V20 smartphone. This may be due to better antenna9 ofand 15 dBHz,GNSS chipobviously of the betterMate30 than smartphone. those with the V20 smartphone. This may be due to better antenna and GNSS chip of the Mate30 smartphone.

Figure 6. The mean carrier-to-noise ratios of the first frequency with the V20 smartphone (cyan), the Figure 6. The mean carrier-to-noise ratios of the first frequency with the V20 smartphone (cyan), the FigureMate30 6. smartphone The mean carrier-to-noise (orange) and the ratios geodetic of the receiver first frequency Trimble with R8 (blue)the V20 in smartphonethe first (a), the(cyan), second the Mate30 smartphone (orange) and the geodetic receiver Trimble R8 (blue) in the first (a), the second (b) Mate30(b) and thesmartphone third (c) experiments.(orange) and the geodetic receiver Trimble R8 (blue) in the first (a), the second and(b) and the thirdthe third (c) experiments. (c) experiments. Figure 7 shows the typical relationships between the carrier-to-noise ratio and the elevation Figure7 shows the typical relationships between the carrier-to-noise ratio and the elevation angle angleFigure for the 7 showsthree devices.the typical The relationships carrier-to-noise between ratio withthe carrier-to-noise the geodetic re ratioceiver and increases the elevation as the for the three devices. The carrier-to-noise ratio with the geodetic receiver increases as the elevation angleelevation for anglethe three increases, devices. while The for carrier-to-noise the two smartphones, ratio with the the correlation geodetic rebetweenceiver increases carrier-to-noise as the angle increases, while for the two smartphones, the correlation between carrier-to-noise ratio and elevationratio and elevationangle increases, angle is while not obvious. for the Thistwo couldsmartphones, be explained the correlation by the differe betweennt polarization carrier-to-noise modes. elevation angle is not obvious. This could be explained by the different polarization modes. Instead of ratioInstead and of elevation the right-handed angle is not circular obvious. polarization, This could thebe explainedlinear polarization by the differe is adoptednt polarization by the built-in modes. the right-handed circular polarization, the linear polarization is adopted by the built-in GNSS antennas InsteadGNSS antennas of the right-handed of the smartphones. circular polarization, Therefore, thethe linearsmartphones polarization are more is adopted vulnerable by the to built-in signal of the smartphones. Therefore, the smartphones are more vulnerable to signal interferences. GNSSinterferences. antennas of the smartphones. Therefore, the smartphones are more vulnerable to signal interferences.

Figure 7. The carrier-to-noise ratios of L1 (blue), L5 or L2 (orange) code measurements of G26 satellite withFigure the 7. Mate30 The carrier-to-noise (a), the V20 (b )ratios and theof TrimbleL1 (blue), R8 L5 (c )or with L2 respect(orange) to thecode satellite measurements elevation of in G26 the Figure 7. The carrier-to-noise ratios of L1 (blue), L5 or L2 (orange) code measurements of G26 ﬁrstsatellite experiment. with the Mate30 (a), the V20 (b) and the Trimble R8 (c) with respect to the satellite elevation satellitein the first with experiment. the Mate30 (a), the V20 (b) and the Trimble R8 (c) with respect to the satellite elevation Inin the addition, first experiment. through the comparison between the carrier-to-noise ratios of L1 and L5 observations collectedIn addition, by the smartphones, through the it iscomparison found that between the carrier-to-noise the carrier-to-noise ratios of the ratios L5 observations of L1 and areL5 In addition, through the comparison between the carrier-to-noise ratios of L1 and L5 equivalentobservations to orcollected even better by thanthe smartphones, those of the L1 it observations is found that in thethecases carrier-to-noise of low elevations. ratios Itof can the also L5 observations collected by the smartphones, it is found that the carrier-to-noise ratios of the L5 beobservations inferred that are theL5 equivalent signal outperformsto or even better the L2 than signal those in anti-jammingof the L1 observations in the cases in ofthe low cases elevations. of low observations are equivalent to or even better than those of the L1 observations in the cases of low However,elevations. the It can average also carrier-to-noisebe inferred that ratiostheL5 ofsignal the L5 outperforms signals received the L2 by signal the smartphones in anti-jamming are around in the elevations. It can also be inferred that theL5 signal outperforms the L2 signal in anti-jamming in the 4 dBHz lower than those of the L1 signals. This might be due to the imperfect multi-frequency antenna design of the smartphones, and further study is needed. Sensors 2020, 20, 5917 10 of 15 Sensors 2020, 20, x FOR PEER REVIEW 10 of 15

3.3. Multipathcases of low Eff elevations.ect However, the average carrier-to-noise ratios of the L5 signals received by the smartphones are around 4 dBHz lower than those of the L1 signals. This might be due to the Theimperfect propagation multi-frequency direction, antenna amplitude design andof th phasee smartphones, of GNSS and signal further are study prone is toneeded. be aff ected by the reflections of the surrounding at the antenna, and the reflected signals can cause multipath effects [29]. The observing3.3. Multipath environments Effect for smartphone users are complicated, and it is inconvenient for the antenna ofThe a propagation smartphone direction, to suppress amplitude the multipathand phase of error GNSS through signal are hardware prone to be like affected the choke by the ring. Therefore,reflections the multipathof the surrounding error usually at the playsantenna, a dominantand the reflected role in signals the code can measurementcause multipath collected effects by smartphone[29]. The [ 9observing]. Since the environments multipath errorfor smartphone of code measurement users are complicated, is as 200 and times it is large inconvenient as that of for carrier phasethe [30 antenna,31], we of focus a smartphone on the multipath to suppress error the ofmult theipath code error measurement through hardware in the study.like the choke ring. WhenTherefore, dual-frequency the multipath observations error usually plays are available, a dominant the role code in multipaththe code measurement errors can becollected studied by with the multipathsmartphone combination, [9]. Since the which multipath can be error expressed of code as measurement is as 200 times large as that of carrier phase [30,31], we focus on the multipath error of the code measurement in the study. When dual-frequency observations are available,2 + 2 the code2 multipath errors can be studied with fi fj 2 fj the multipath combination, whichMP ican= Pbej expressed Φasi + Φj (5) − f 2 f 2 f 2 f 2 i j i j 𝑓 −+ 𝑓 2𝑓− 𝑀𝑃 =𝑃 − 𝛷 + 𝛷 (5) 𝑓 − 𝑓 𝑓 − 𝑓 where the subscripts i and j (i , j) denote diff erent frequency bands, and f is the frequency value. The combinationwhere the subscripts expressed 𝑖 byand Equation 𝑗 (𝑖 ≠ 𝑗) (5)denote mainly different contains frequency the multipath bands, errorand 𝑓 of is corresponding the frequency code measurementvalue. The and combination the linear combinationexpressed by ofEquation the ambiguities. (5) mainly If nocontains cycle slipthe occurs,multipath the error ambiguities of are consideredcorresponding constants code measurement and can be removed and the linear through comb averagingination of over the ambiguities. epochs [32]. If The no subscriptscycle slip for receiveroccurs, and the satellite ambiguities have been are omittedconsidered here constant for simplicity.s and can be removed through averaging over epochs [32]. The subscripts for receiver and satellite have been omitted here for simplicity. Shown in Figure8 are the standard deviations of L1 frequency code multipath error of the three Shown in Figure 8 are the standard deviations of L1 frequency code multipath error of the devices in the three experiments. The standard deviations of code multipath errors of the Mate30 three devices in the three experiments. The standard deviations of code multipath errors of the and theMate30 V20 and are the as V20 ten are times as ten large times as large that as of that the of Trimble the Trimble R8. R8. This This phenomenon phenomenon showsshows that the two smartphonesthe two smartphones have disadvantages have disadvantages in suppressing in suppressing the the multipath multipath eff effects.ects.

FigureFigure 8. The 8. The standard standard deviations deviations valuesvalues of of L1 L1 frequency frequency code code multipath multipath error errorof the V20 of the (cyan), V20 the (cyan), the Mate30Mate30 (orange)(orange) and and the the Trimble Trimble R8 R8 (blue) (blue) during during the first the ﬁrst(a), the (a), second the second (b) and (b the) and third the (c) third (c) observingobserving periods. periods.

FiguresFigures9 and 9 10 and show 10 show the typical the typical standard standard deviations deviations values values and and the the multipath multipath errors errors time time series of L1,series L5 or of L2 L1, code L5 measurements or L2 code measurements with the three with devices. the three It can devices. be seen It thatcan be the seen code that multipath the code errors of themultipath two smartphones errors of the vary two from smartphones4 and 4vary m, from and that−4 and the 4 codem, and measurements that the code measurements with the geodetic − receiverwith are the less geodetic aﬀected receiver by the are multipath less affected error. by the It suggestsmultipath that error. the It multipathsuggests that error the shouldmultipath be one error should be one of the main factors limiting the accuracy of PPP with the smartphones. of the main factors limiting the accuracy of PPP with the smartphones. Meanwhile, the standard Meanwhile, the standard deviationsof the code multipath errors of L1 and L5 code measurements deviationsof the code multipath errors of L1 and L5 code measurements are about 2 m for smartphones, are about 2 m for smartphones, and L5 code measurements are generally less affected by multipath and L5Sensorserrors code 2020than measurements, 20 L1, x FORcode PEER measurements. REVIEW are generally less aﬀected by multipath errors than L1 code measurements.11 of 15

FigureFigure 9. The 9. standardThe standard deviations deviations of L1 of (orange), L1 (orange), L5 orL5 L2 or (blue) L2 (blue) code code multipath multipath errors errors with with the the Mate30 (a), theMate30 V20 ((ba)), andthe V20 the ( Trimbleb) and the R8 Trimble (c) in the R8 ﬁrst(c) in experiment.the first experiment.

Figure 10. The multipath errors of L1 (blue), L5 or L2 (orange) code measurements of G26 satellite with the Mate30 (a), the V20 (b) and the Trimble R8 (c) in the first experiment. The standard deviations of the code multipath errors are shown with the corresponding colors in meters.

4. PPP Results In this paper, a single-frequency PPP processing strategy based on ionosphere constraints is employed. Shown in Table 2 are the setting details of the single-frequency PPP for smartphones. The products of satellite orbit, satellite clock and Earth rotation are downloaded from MGEX data center (http://www.cddis.gsfc.nasa.gov/). Ionospheric delay products are downloaded from CODE. And ionospheric delay is constrained by GIM products of CODE.

Table 2. The detailed settings of the PPP with smartphone.

Setting Items Details Observations Single-frequency pseudorange and carrier phase Satellite systems GPS and BDS Satellite orbit and clock Precise orbit and clock product Ionospheric delay Estimated as a parameter The hydrostatic delay is corrected by the Saastamoinen model and the wet Tropospheric delay delay is estimated as a parameter Effects of relativity and Earth rotation files earth rotation Weighting method Satellite elevation angle Integer ambiguities of Estimating float solution carrier phase Cutoff satellite elevation 10° angle Parameters estimation Standard static Kalman filter method

In order to compare the positioning results with the different strategies and to validate the abovementioned method, three experiments were conducted with different settings. The details of these projects are shown in Table 3.

Sensors 2020, 20, x FOR PEER REVIEW 11 of 15

Figure 9. The standard deviations of L1 (orange), L5 or L2 (blue) code multipath errors with the Sensors 2020, 20, 5917 11 of 15 Mate30 (a), the V20 (b) and the Trimble R8 (c) in the first experiment.

Figure 10. The multipathmultipath errorserrors of of L1 L1 (blue), (blue), L5 L5 or or L2 L2 (orange) (orange) code code measurements measurements of G26 of satelliteG26 satellite with withthe Mate30 the Mate30 (a), the (a V20), the (b) V20 and the(b) Trimbleand the R8 Trimble (c) in the R8 first (c) experiment.in the first Theexperiment. standard The deviations standard of deviationsthe code multipath of the code errors multipath are shown errors with are the shown corresponding with the corresponding colors in meters. colors in meters. 4. PPP Results 4. PPP Results In this paper, a single-frequency PPP processing strategy based on ionosphere constraints is In this paper, a single-frequency PPP processing strategy based on ionosphere constraints is employed. Shown in Table2 are the setting details of the single-frequency PPP for smartphones. employed. Shown in Table 2 are the setting details of the single-frequency PPP for smartphones. The The products of satellite orbit, satellite clock and Earth rotation are downloaded from MGEX data products of satellite orbit, satellite clock and Earth rotation are downloaded from MGEX data center center (http://www.cddis.gsfc.nasa.gov/). Ionospheric delay products are downloaded from CODE. (http://www.cddis.gsfc.nasa.gov/). Ionospheric delay products are downloaded from CODE. And And ionospheric delay is constrained by GIM products of CODE. ionospheric delay is constrained by GIM products of CODE. Table 2. The detailed settings of the PPP with smartphone. Table 2. The detailed settings of the PPP with smartphone. Setting Items Details Setting Items Details Observations Single-frequency pseudorange and carrier phase Observations Single-frequency pseudorange and carrier phase Satellite systems GPS and BDS SatelliteSatellite systems orbit and clock Precise GPS orbitand andBDS clock product Satellite orbitIonospheric and clock delay Precise orbit Estimated and clock as a parameterproduct Ionospheric delay The hydrostatic Estimated delay is corrected as a parameter by the Saastamoinen model and Tropospheric delay The hydrostatic delay isthe corrected wet delay by is the estimated Saastamoinen as a parameter model and the wet Tropospheric delay Effects of relativity and earth rotationdelay is estimated Earth rotationas a parameter files Effects ofWeighting relativity methodand Satellite elevation angle Integer ambiguities of carrier phaseEarth Estimating rotation float files solution earth rotation Cutoff satellite elevation angle 10◦ WeightingParameters method estimation method Satellite Standard elevation static Kalmanangle filter Integer ambiguities of Estimating float solution carrier phase CutoffIn order satellite to compare elevation the positioning results with the different strategies and to validate the 10° abovementionedangle method, three experiments were conducted with different settings. The details of theseParameters projects are estimation shown in Table3. Standard static Kalman filter method Table 3. The differences of detailed settings of positioning projects, where improved PPP employs Inionosphere-constrained order to compare the single-frequency positioning PPPresults model with described the different in Section strategies2. The other and settings to validate of the the abovementionedthree projects employedmethod, three the setting experiments proposed were in Table co2nducted. with different settings. The details of these projects are shown in Table 3. Processing Strategies Options SPP Traditional PPP Improved PPP The preprocessing strategy Code preprocessing No Gross error elimination [22] proposed in Section 2.2 The strategy based on The strategy proposed in Cycle slip detection No satellite lock out [22] Section 2.3 Filtering processing No Yes Yes

The standard deviations of positioning errors of the Mate30 using diﬀerent projects are shown in Table4. Considering that the horizontal position of smartphones is more widely used [ 10–13], the horizontal accuracy of smartphones was only recorded and analyzed. The standard deviations of positioning errors in the East and North direction of the Mate30 using the SPP and traditional PPP projects are about 2–4 m. And that of the improved PPP project is less than 1 m. Table4 show that the Sensors 2020, 20, 5917 12 of 15 positioning accuracy of the Mate30 using the improved PPP project is obviously improved compare with the other two projects.

Table 4. The standard deviations of positioning errors in east (E) and north (N) directions.

Standard Deviations of Positioning Errors (m) Time Periods SPP Traditional PPP Improved PPP ENENEN 1st 3.43 2.73 3.14 2.56 0.48 0.59 2nd 2.73 3.35 3.16 3.40 0.54 0.49 3rd 2.00 2.58 2.13 2.31 0.23 0.20

The time series of horizontal positioning errors are shown in Figure 11. When the improved PPP model is employed, the positioning errors in E and N directions during all of the three time periods can converge to less than 1 m and stay relatively stable. With the SPP and traditional PPP model, the positioning errors in E and N directions exceed 2 m, and the positioning results are unstable. In general, the performance of SPP is highly related to the data quality of code pseudorange. The positioning accuracies with the traditional PPP and SPP models are at the same level, although precise orbit and clock products are adopted in the PPP model. The poor performance of the traditional PPP in the above experiments could be explained from two aspects. On one hand, the quality of the code measurements collected by the Mate30 is poor. One the other hand, frequent loss of lock for the satellites make it difficult for the Kalman filter to work with the smartphones. Compared with the traditional PPP model, the improved PPP model is able to smooth code pseudorange with Doppler andSensors to 2020 detect, 20, x cycle FOR PEER slip more REVIEW eff ectively, so the positioning accuracy is significantly improved. 13 of 15

Figure 11.11. The E (blue) and N (orange) error components of of SPP SPP ( (aa,,dd,,gg),), the the traditional PPP ( b,e,h) and the improved improved PPP PPP (c (c,f,f,i,)i )results results with with the the Mate30 Mate30 during during the the first ﬁrst (a– (ca),– cthe), the second second (d–f ()d –andf) and the thethird third (g– (i)g –observingi) observing periods. periods. The The standa standardrd deviations deviations of of positionin positioningg errors errors are are shown shown with the corresponding colorcolor inin meters.meters.

5. Conclusions and Discussion In this paper, the GNSS raw observations of two smartphones are analyzed through synchronous observations with a geodetic receiver. To study the positioning performances with the smartphones, experiments are conducted with a V20 smartphone and a Mate30 smartphone, as well as a Trimble R8 receiver. With the ionosphere-constrained single-frequency PPP strategy, an improvement in smartphone positioning is achieved. The horizontal positioning accuracy better than 1 m can be reached with the Mate30 smartphone. Compared with a Trimble R8 geodetic receiver and a V20 smartphone, the GNSS performance of the Mate30 is evaluated. Firstly, there is little difference between the number of observable satellites in the first frequency of the Mate30 and the Trimble R8 receiver. The number of observable satellites with dual-frequency measurements available of the Mate30 and the V20 is not enough for dual-frequency PPP. The satellites lock loss of the Mate30 and the V20 is more frequent than the Trimble R8. The satellite tracking of smartphones is related to its chip. Secondly, the carrier-to-noise ratio of the smartphones is worse than that of the Trimble R8. And the carrier-to-noise ratio of the Mate30 is better than that of the V20. The carrier-to-noise ratio of smartphones has no significant relationship with elevation angle of satellites. Meanwhile, the multipath effect of smartphones is more serious than the Trimble R8. The standard deviations of code multipath errors of the Mate30 and the V20 are about ten times that of the Trimble R8. The performances of the smartphones against multipath effects of are worse than that of the geodetic receiver. Since the number of navigation satellite with dual-frequency signals observed by smartphones is not enough for dual-frequency PPP, ionosphere-constrained single-frequency PPP model is used for smartphones. In this study, the standard deviations of horizontal positioning errors with the Mate30 are less than 1 m and the Mate30 achieves a relatively stable positioning result. Comparing the different positioning projects, we believe that there are two main reasons for the poor performance using the traditional PPP project of smartphones. For the current smartphones, the data qualities of the code measurements are relatively poor, and the losses of lock for satellites occur frequently. The ionosphere-constrained single-frequency PPP model along with the cycle slip

Sensors 2020, 20, 5917 13 of 15

5. Conclusions and Discussion In this paper, the GNSS raw observations of two smartphones are analyzed through synchronous observations with a geodetic receiver. To study the positioning performances with the smartphones, experiments are conducted with a V20 smartphone and a Mate30 smartphone, as well as a Trimble R8 receiver. With the ionosphere-constrained single-frequency PPP strategy, an improvement in smartphone positioning is achieved. The horizontal positioning accuracy better than 1 m can be reached with the Mate30 smartphone. Compared with a Trimble R8 geodetic receiver and a V20 smartphone, the GNSS performance of the Mate30 is evaluated. Firstly, there is little difference between the number of observable satellites in the first frequency of the Mate30 and the Trimble R8 receiver. The number of observable satellites with dual-frequency measurements available of the Mate30 and the V20 is not enough for dual-frequency PPP. The satellites lock loss of the Mate30 and the V20 is more frequent than the Trimble R8. The satellite tracking of smartphones is related to its chip. Secondly, the carrier-to-noise ratio of the smartphones is worse than that of the Trimble R8. And the carrier-to-noise ratio of the Mate30 is better than that of the V20. The carrier-to-noise ratio of smartphones has no significant relationship with elevation angle of satellites. Meanwhile, the multipath effect of smartphones is more serious than the Trimble R8. The standard deviations of code multipath errors of the Mate30 and the V20 are about ten times that of the Trimble R8. The performances of the smartphones against multipath effects of are worse than that of the geodetic receiver. Since the number of navigation satellite with dual-frequency signals observed by smartphones is not enough for dual-frequency PPP, ionosphere-constrained single-frequency PPP model is used for smartphones. In this study, the standard deviations of horizontal positioning errors with the Mate30 are less than 1 m and the Mate30 achieves a relatively stable positioning result. Comparing the different positioning projects, we believe that there are two main reasons for the poor performance using the traditional PPP project of smartphones. For the current smartphones, the data qualities of the code measurements are relatively poor, and the losses of lock for satellites occur frequently. The ionosphere-constrained single-frequency PPP model along with the cycle slip detection method proposed in this work, to a certain extent, circumvents the above disadvantages and has considerable applicability on the smartphones. The GNSS chip and antenna performance of smartphones is worse than that of geodetic receivers because the design of smartphones needs to consider the beauty and portability. This leads to frequent satellite lock loss, poor code pseudorange quality and serious multipath effects. Therefore, improving hardware quality and algorithm research are effective methods to improve smartphones positioning accuracy. Although the dual-frequency PPP model is unrealistic for smartphones at present due to their incompetence in collecting dual-frequency measurements, it is expected that the performance of PPP with smartphones will be significantly improved in the future with both the rapid development of BDS and the iterative upgrades of smartphones.

Author Contributions: Conceptualization, G.W. and Y.B.; Methodology, G.W. and M.L.; Software, Y.B. and Z.Y.; Validation, Y.B. and Z.Y.; Writing—original draft preparation, Y.B. and Q.Y.; Writing—review and editing, G.W. and Y.C. All authors have read and agreed to the published version of the manuscript. Funding: This work was sponsored by the funds of Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University (Grant No. 18-01-06), the National Science Foundation of China (Grant No. 41804033 and 42004073), and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUGL180831). Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Li, X.; Ge, M.; Dai, X.; Ren, X.; Fritsche, M.; Wickert, J.; Schuh, H. Accuracy and reliability of multi-GNSS real-time precise positioning: GPS, GLONASS, BeiDou, and Galileo. J. Geod. 2015, 89, 607–635. [CrossRef] Sensors 2020, 20, 5917 14 of 15

2. Yang, Q.; Li, S. Analysis of the impact of open GNSS original measurement on the positioning accuracy of Android platform. J. Navig. Position. 2019, 7, 115–120. 3. Google GPS Measurement Tools. Available online: https://github.com/google/gps-measurement-tools/tree/ master/GNSSLogger (accessed on 1 October 2019). 4. Logging of GNSS Raw Data on Android. Available online: http://www.geopp.de/logging-of-gnss-rawdata- on-android/ (accessed on 1 November 2019). 5. NSL Launches a New Free Android App as Part of FLAMINGO: Discover rinexON [Z/OL]. [2019–04–17]. Available online: https://www.flamingognss.com/rinexon (accessed on 1 November 2019). 6. Gim, J.; Park, K. Comparison of Positioning Accuracy Using the Pseudorange from Android GPS Raw Measurements. J. Korea Narvig Inst. 2017, 21, 514519. 7. Realini, E.; Caldera, S.; Pertusini, L.; Daniele, S. Precise GNSS Positioning Using Smart Devices. Sensors 2017, 17, 2434. [CrossRef] 8. Zhang, X.; Tao, X.; Zhu, F.; Wang, F. Quality assessment of GNSS observations from an Android N smartphone and positioning performance analysis using time-differenced filtering approach. GPS Solut. 2018, 22, 70. [CrossRef] 9. Hakansson, M. Characterization of GNSS observations from a Nexus 9 Android tablet. GPS Solut. 2019, 23, 21. [CrossRef] 10. Li, J.; Bi, J.; Li, D.; Zhou, W.; Zhu, S. Research on real-time BDS+GPS dual system wide area differential positioning technology under Android platform. Surv. Mapp. Bull. 2017, 12–15. [CrossRef] 11. Specht, C.; Dabrowski, P.S.; Pawelski, J.; Specht, M.; Szot, T. Comparative analysis of positioning accuracy of GNSS receivers of Samsung Galaxy smartphones in marine dynamic measurements. Adv. Space Res. 2019, 63, 3018–3028. [CrossRef] 12. Dabove, P.; Pietra, C.D. Towards high accuracy GNSS real-time positioning with smartphones. Adv. Space Res. 2019, 63, 94–102. [CrossRef] 13. Wanninger, L.; Hesselbarth, A. GNSS code and carrier phase observations of a Huawei P30 smartphone: Quality assessment and centimeter-accurate positioning. GPS Solut. 2020, 24, 64. [CrossRef] 14. World’s First Dual-Frequency GNSS Smartphone Hits the Market. Available online: https://www.gsa.europa.eu/ newsroom/news/world-s-first-dual-frequency-gnss-smartphone-hits-market (accessed on 1 October 2019). 15. Zhao, S.; MI, J.; Xu, Y.; Zhao, Z. Data quality and positioning accuracy analysis of dual-frequency smart phone GNSS [J/OL]. Surv. Mapp. Sci. 2020, 45, 22–28. 16. Chen, B.; Gao, C.; Liu, Y.; Lu, Y. Quality analysis on raw GNSS measurements of Android mobile terminals. J. Navig. Position. 2019, 7, 87–95. 17. Chen, B.; Gao, C.; Liu, Y.; Sun, P. Real-time precise point positioning with a Xiaomi MI-8 android smartphone. Sensors 2019, 19, 2835. Available online: https://www.mdpi.com/1424-8220/19/12/2835 (accessed on 29 August 2019). 18. Shi, X. Continuous Smoothing Positioning Algorithm Based on GNSS Observations of Smartphones. Master’s Thesis, Wuhan University, Wuhan, China, 2019. 19. Wu, Q.; Sun, M.; Zhou, C.; Zhang, P. Precise Point Positioning Using Dual-Frequency GNSS Observations on Smartphone. Sensors 2019, 19, 2189. [CrossRef][PubMed] 20. Aihetamu, Y.; Huang, Z.; Wang, Y. Discussion on GPS Single-frequency PPP Ionosphere Delay Correction Model. J. Gansu Sci. 2017, 29, 24–28. 21. Shi, C.; Gu, S.; Lou, Y.; Ge, M. An Improved Approach to Model Ionospheric Delays for Single-frequency Precise Point Positioning. Adv. Space Res. 2012, 49, 1698–1708. [CrossRef] 22. Zhou, F.; Dong, D.N.; Li, W.W.; Jiang, X.Y.; Wickert, J.; Schuh, H. GAMP: An open-source software of multi-GNSS precise point positioning using undifferenced and uncombined observations. GPS Solut. 2018, 22, 33. [CrossRef] 23. Kouba, J. A Guide to Using International GNSS Service (IGS) Products. Available online: http://iacc.igs.org/ UsingIGS-ProductsVer21.pdf (accessed on 10 September 2019). 24. Xiao, Q.; Gu, S.; MI, J.; Chen, B.; Chen, C. Doppler smoothed pseudorange single point positioning accuracy analysis for smartphones. Surv. Mapp. Sci. 2020, 45, 11–17. 25. Zhao, J.J.; Hernandez-Pajares, M.; Li, Z.S.; Wang, L.; Yuan, H. High-rate Doppler-aided cycle slip detection and repair method for low-cost single-frequency receivers. GPS Solut. 2020, 24, 80. [CrossRef] Sensors 2020, 20, 5917 15 of 15

26. Qian, C.; Liu, H.; Zhang, M.; Shu, B.; Xu, L.W.; Zhang, R.F. A Geometry-Based Cycle Slip Detection and Repair Method with Time-Differenced Carrier Phase (TDCP) for a Single Frequency Global Position System (GPS) plus BeiDou Navigation Satellite System (BDS) Receiver. Sensors 2016, 16, 2064. [CrossRef] 27. Zangeneh-Nejad, F.; Amiri-Simkooei, A.R.; Sharifi, M.A.; Asgari, J. Cycle slip detection and repair of undifferenced single-frequency GPS carrier phase observations. GPS Solut. 2017, 21, 1593–1603. [CrossRef] 28. International GNSS Service (IGS), RINEX Working Group and Radio Technical Commission for Maritime Services Special Committee 104 (RTCM-SC104). The Receiver Independent Exchange Format. Available online: ftp://igs.org/pub/data/format/rinex303.pdf (accessed on 10 September 2019). 29. Wu, D.; Wang, L.; Zhang, Q. Implementation and verification analysis of GNSS data quality evaluation software. J. Surv. Mapp. Sci. Technol. 2015, 32, 344–348. 30. Bilich, A. Mapping the GPS multipath environment using the signal-to-noise ratio. Radio Sci. 2008, 42, 1–16. [CrossRef] 31. Axelrad, P.; Comp, C.; Macdoran, P.F. SNR-Based multipath error correction for GPS differential phase. IEEE Trans. Areospace Electron. Syst. 1996, 32, 650–660. [CrossRef] 32. Zhang, X.; Ding, L. Quality Analysis of the Second Generation Compass Observables and Stochastic Model Refining. Geomat. Inf. Sci. Wuhan Univ. 2013, 38, 832–836.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional aﬃliations.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).