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The announcement comes from Chinese social media app Weibo , where Xiaomi posted an advert for a September 24 launch event for the two new phones. Translated from Chinese, the poster states: "Exploring a possibility for the Future.

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Xiaomi 5G new product launch. We were expecting the Mi Mix 4 to be 5G compatible, since the Mi Mix 3 had a 5G version launch worldwide, but the Mi 9 Pro is a name we haven't heard before. It'll likely be an improved version of Xiaomi Mi 9 with specs but not design closer to the Mi 9T Pro. By bringing out two new 5G phones in the same year as the Mi Mix 3 5G, it shows the company is committed to its vision of a 5G future. Currently the only company with more 5G smartphones out is Samsung.

In addition, no one has made improvements in positioning strategy for mobile phones. Our experiment found that the differences between pseudorange observations and carrier phase observations of smartphones are not fixed this phenomenon exists in all the three smartphones tested. We believe that it is necessary to modify the positioning strategy accordingly, and the experimental results verify our thought. In this work, we use a single-frequency PPP strategy that estimates two clock biases of smartphone, a real-time high precision smartphone positioning is achieved.

The accuracy of PPP should be centimeter-level, which is hard to achieve on a mobile phone until now. Because the method we used is same with PPP method, we also call it precise point positioning. The experimental setup adopted is described in Section 2.

The preliminary analysis helps us determine the specific positioning methodology are reported in Section 3. The PPP methodology followed to conduct our experiment are elaborated in Section 4. The experimental results are reported in Section 5. The conclusion and discussion of this work is in Section 6. The main device used in this experiment is a Xiaomi MI 8 smartphone. Three datasets were collected in total with a sampling rate of 1 s. The first and third datasets were in the same site: the top of the teaching building No.

This site is in a low multipath environment. The first dataset was collected on July 12, , over a time span of about three hours. The devices used were a Huawei P10 smartphone, a Huawei Honor 9 smartphone, and two geodetic receivers.

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The third dataset was collected on October 29, , over a time span about one hour, and the devices used are a Xiaomi MI 8 smartphone and a geodetic receiver. Mobile phones and geodetic receivers at the site of first and third dataset. There is a plastic board on the top of the middle tripod, and smartphones are placed on the board. The second dataset was collected on October 19, , and the site is the GE01 control point in Southeast University Jiulonghu Campus, as shown in Figure 2.

The device used is a Xiaomi MI 8 smartphone.

Five time periods were observed, about 6 minutes each time. The precise coordinates of this site are known, and the observations are used for positioning tests. The precise coordinates of this control point are obtained by a geodesic receiver through the network RTK positioning method. We conducted a preliminary analysis on the obtained GNSS raw observations and determined the specific positioning methodology accordingly. The pseudorange and carrier phase observations BDS 02 satellite of a geodetic receiver and a Huawei P10 smartphone. The actual acquired carrier phase of smartphones is a set of data that is cumulatively incremented from 0 m at the beginning of observation, thus the values are small.

In this figure, the mobile phone carrier phase values are added a constant.

In panel a of Figure 3 , the blue and red lines are coincident so only the red line is visible , which indicates that the pseudorange and carrier phase observations in meters of the geodetic receiver are consistent. In fact, the difference between the two values is equal to the carrier phase integer ambiguity in meters.

However, in panel b , the orange and purple lines are not consistent and have different slopes, which indicates that the differences between the pseudorange and the carrier phase observations of Huawei P10 are not fixed. This property is different from the geodetic receivers, which affects the use of carrier phase measurements of smartphone. Particularly for the Xiaomi MI 8 smartphone, the dual-frequency raw observations Galileo 03 satellite and its change rate are shown in Figure 4.

In panel a , P1 and P5 denote pseudorange observations of two frequencies E1 and E5, L1 and L5 denote corresponding carrier phase observations. In panel b , P1, P5, L1, and L5 denote a corresponding change rate. Obviously, the pseudorange observations of two frequencies are coincident the corresponding two lines are coincident , and the carrier phase observations of two frequencies are also coincident. However, it is evidently that the differences between pseudorange observations and carrier phase observations are not fixed, which is same with Huawei P10 and Huawei Honor 9.

Panel b shows that the change rate of observations also exists the same phenomenon. At the same time, P5 is more stable than P1 according panel b , which means the data quality of P5 is better obviously. The observations Galileo 03 satellite and its change rate of a Xiaomi MI 8 smartphone. As shown in Figure 4 , the differences between the smartphone pseudorange and carrier phase observations are gradually increased, and the difference values are large.

At the same time, the differences between pseudorange change rate and carrier phase change rate are relatively stable. The RMS of smartphone pseudorange observations exceeds a few meters, thus the difference value between the change rate of pseudorange and the change rate of carrier phase observations should have an amplitude of several meters too. This makes it difficult to assess the degree of agreement between different satellites observations, thus the difference values are calculated with a window of epochs take the average value of epochs. There are a total of 26 lines in panel a of Figure 5 , and a total of 23 lines in panel b , the different colored lines represent different satellites.

Although there are some deviations, most of the lines are coincident, and the lines of Xiaomi MI 8 are more coincident. Which indicates that the differences between the change rate of pseudorange observations and the change rate of carrier phase observations of all satellites are consistent. It also can be seen that the differences of change rate are slowly changing during the observation period, and the variation range is about several meters within one hour.

Since the influence of the device clock bias on all satellites observations is the same, we believe that estimating two clock biases in positioning process can effectively weaken the impact of the phenomenon that the differences between the pseudorange and the carrier phase observations of mobile phone are not fixed.

During the first and third dataset collections, mobile phones and geodetic GNSS receivers were synchronized at the same location. The accuracy of the carrier phase observation of geodetic receivers is much better than the pseudorange observations of smartphones. We took the carrier phase observations of the geodetic receivers as the standard value and calculated the RMS of pseudorange observations of smartphones.

The specific steps are as follow:. Performing linear regression to eliminate systematic deviation such as the influence of geodetic receiver clock bias on the difference values within a certain time window epochs , obtaining the regression residuals;. For the carrier phase observations, by calculating the RMS of carrier phase observations change rate of each device also with the window of epochs, and the linear regression was also performed , the qualities of carrier phase observations of tested devices are compared and analyzed.

According to the preliminary analysis, there are two clock biases of smartphone that need to be estimated in the positioning process.

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Taking the GPS as an example, the observation equations can be described by:. There are too few Galileo satellites and GPS satellites with L5 signals observed by mobile phones, however, the parameters to be estimated are too many, and thus the PPP positioning model used is a single-frequency non-difference model. Using precise ephemeris and precise clock bias files to reduce the orbital errors and satellite clock biases, weakening the ionospheric delay error with corresponding product, the observation equation was simplified as in Reference [ 16 ]:.

The vector of parameters to be estimated is:. The parameters estimation method used is the standard static Kalman filter [ 16 ]. In particular, since the vector of parameters to be estimated is modified, the matrix of observation coefficients in the filtering process needs to be modified accordingly.

When the observations equation is as shown in Equation 8 , the coefficient matrix should be as shown in Equation 9.

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The specific PPP positioning settings are shown in Table 1 below. Moreover, the predicted GIM product used in this study has a worse correction effect than the final product, which is an important factor affecting the performance of our positioning experiments. All the IGS data products used were predicted products, indicating that the method used in this work is suitable for real-time positioning. Using the method described in Section 3 , calculating the RMS of pseudorange observations of each mobile phone, the results are shown in Table 2 below.

Table 2 and Table 3 show that the quality of the raw observations of Xiaomi MI 8 is obviously improved compare with single-frequency mobile phones. In addition, the observed GPS satellites with L5 signal are Using the PPP method detailed in Section 3 , positioning tests was performed with a Xiaomi 8 smartphone. In order to evaluate the impact of using different constellation combinations on the positioning results, we used a variety of GNSS systems combinations for testing. In general, as the number of constellations used increases, the positioning performance gradually improves.

Thus, the performance of single constellation positioning is poor. Since the number of Galileo satellites observed by mobile phones is small, the positioning performance is not obviously improved after adding Galileo data. For GLONASS, after adding the its data, the positioning performance of some time periods can be improved, and some time periods the 1st and 5th time periods are badly affected.