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.
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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. 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. 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.
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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.
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The average horizontal RMS positioning error is 0. When using GPS, BDS, and Galileo data, after observing for a section of time, the East and North direction errors of all the five time periods can converge to less than 1 m and achieve a relatively stable positioning result; but the U direction positioning result is not stable enough. When using GPS data only, the positioning results are more unstable, and the systematic errors are also greater. The loss of positioning accuracy in the U direction is more obvious compared to the E and N directions.
We believe that there are two reasons for the poor performance of single system positioning: the number of satellites observed by mobile phones is not enough, and the quality of smartphone raw GNSS observations is poor. This is consistent with the idea of surveying adjustment: when the accuracy of a single measurement is poor, a large number of repeated observations are needed to improve the overall accuracy. If the number of available satellites continues to increase, the positioning performance may be further improved.
The accuracy of 1 m can meet the needs of most non-professional fields, even some professional fields with low precision requirements [ 13 ]. The time required for positioning errors in N and E to both be less than 1 m and the subsequent epoch error to no longer exceed 1 m is counted, as shown in Table 5. The time required for positioning errors in N and E directions to be under 1 m of each time period is less than 30 s, which indicates that the modified PPP strategy can be applied to real-time positioning and provides a slightly delayed high-precision positioning result.
The positioning performance achieved in this work is the best real-time positioning performance that can be achieved with ordinary smartphones until now. The time required for positioning errors in N and E directions less than 1 m of all time periods. We also performed positioning tests using the ordinary PPP strategy which estimates single device time bias and the positioning errors are of the level of several meters.
As reported by Wu et al. This indicates that evaluating two clock bias for the Xiaomi MI 8 smartphone is valid. In this paper, we compared and analyzed the quality of GNSS raw observations of different smartphones. Using a modified single-frequency PPP strategy, a real-time high precision smartphone positioning is achieved.
The average RMS of the carrier phase observation change rates of these three smartphones are 5. By analyzing the GNSS raw observations, it is found that all the three smartphones tested have the phenomenon that the differences between pseudorange observations and carrier phase observations are not fixed.
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This may be a general problem with smartphones that needs to be modified in the positioning algorithm. Using the real-time single-frequency PPP positioning strategy which estimated double clock biases of smartphone, the results show that using more GNSS systems data can effectively improve positioning performance, but GLONASS data sometimes have a bad effect on positioning performance. The time required for positioning errors in N and E directions to be under 1 m of each time period is less than 30 s, which is the best real-time positioning performance that can be achieved with smartphones.
Compared with the experimental results of Wu et al. However, because we used single-frequency data, the ionospheric errors could not be effectively eliminated, and the predicted IGS products used were predicted products for real-time positioning , so the difference in accuracy is acceptable. More importantly, the time required for high positioning accuracy of our work is much less, which means that our work is more in line with the actual application scenarios of smart phones.
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Besides this, differential positioning, especially RTK positioning, can also effectively improve smartphone positioning performance [ 11 , 12 ]. For this method, the influence on positioning results caused by the phenomenon we found are not clear, which needs further analyses and discussions. The preliminary results show that the method has practical effects, and for ordinary single frequency smartphones, their positioning performance can be improved too. A mobile phone positioning app with high precision can effectively improve the user experience of public and may be applied to some professional work with low precision requirements, such as external annotation and cadastral survey.
However, it can be foreseen that the GNSS chip in the mobile phone is developing rapidly and the updates of Android operating system will also affect the quality of smartphone raw GNSS observations. Whether the future smartphones have the phenomenon that the differences of pseudorange and carrier phase observations are not fixed needs to be determined according to specific analysis. Conceptualization, C. National Center for Biotechnology Information , U.
Journal List Sensors Basel v. This suggests that companies often release updates for their high-priced devices. But, their mid-range and budget devices might not get the love of the latest Android version. Operating system and security updates are an aspect of Android smartphones that get relatively little attention. In our experience researching the industry, we have seen a few brands focusing on this.
And perhaps because manufacturers are not talking about it, consumer awareness is also low. Looking at the report, Nokia has done a fantastic job of keeping their phones updated and secure. However, many brands need to pay much more attention to pushing the latest software and security updates to their phones across different price ranges. Read next: Binance continues crusade for crypto world domination with new dev platform. Read our daily coverage on how the tech industry is responding to the coronavirus and subscribe to our weekly newsletter Coronavirus in Context.
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Only when recording while walking is a jello effect frequently visible in iPhone video clips. Apple has always put a lot of emphasis on the video performance of its devices. This has resulted in huge popularity among the mobile video community and an abundance of choices in terms of mobile video accessories, such as external microphones, video grips, and lighting solutions, as well as video editing apps. In combination with excellent video output quality, this makes the latest top-end iPhone an ideal choice for mobile videographers, journalists, vloggers, and other content creators who want to shoot high-quality video with extremely lightweight and compact equipment.
For more information, detailed test data, and image and video samples, read our full review and article on new camera features on the iPhone 11 Pro Max:. Thanks to its HDR recording and mobile video ecosystem, the iPhone just edges it out, but video shooters who prefer the Android operating system cannot go wrong with the Xiaomi.
Just make sure you can actually get hold of one, as it is not currently available in all markets. Zoom is still one of the very few areas—if not the only area—where conventional compact cameras still have the edge over smartphones. It crushed the competition in our tests and set a new high Zoom sub-score of , thanks to a complex and innovative zoom system that is currently unrivaled. It uses two different focal length telephoto cameras, as well as an imaging pipeline that has been optimized for this unusual setup, to provide high-quality telephoto images across our entire range of tested focal lengths.