# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 1 Introduction  
The Global Navigation Satellite System (GNSS), which was initially limited to military or surveying fields, has gradually expanded into ordinary industrial fields such as navigation and time synchronization. As the price of GNSS chipsets has fallen below US$1 and smartphones have become increasingly popular, the number of GNSS applications, such as car navigation, geo-tagging, and location-based systems, is likely to rise dramatically. Various programs and services use positional information provided by the GNSS modules of smartphones. Indeed, 88% of American smartphone owners now use their handset as a map or navigation device in place of a car navigation kit [1], and the integration of visual reality/artificial reality (VR/AR) into real-world geolocation is expected to be realized in the near future [2]. As the positioning accuracy of GNSS chipsets improves, existing devices are likely to be replaced and location-based services will become further diversified. Automated vehicle identification, accident surveys, and emergency response require the location accuracy to be better than 4 m, and search and rescue and fire management are possible when a device is accurate to within 1-5 m [3]. Land surveys, such as for planning, topographic survey reconnaissance, geologicalsurveys, and epidemiological mapping, require accuracy at the 1-3 m level and could therefore benefit from the use of smartphones [4]. Thus, there is strong interest in enhancing the achievable accuracy of smartphones, but the underlying positioning accuracy has remained unchanged over the past decade. Smartphones are essentially personal computers with various sensors, including a GNSS module. Most smartphones can access the internet and run third-party applications. The first version of the Android smartphone, T-mobile G1, included a 528 MHz ARM 11 CPU and used the 3G or Global Service for Mobile communication (GSM) network. Smartphone-related technologies have expanded at a rapid pace: the latest device, the Samsung Galaxy S7, contains a quad-core 2.3 GHz CPU and receives data streams over Long Term Evolution (LTE) networks. The performance of GNSS chipsets has also improved. Assisted GPS (A-GPS) can obtain a faster location fix by acquiring almanac and ephemeris information via the cellular network. To provide more accurate and responsive location data to mobile users, even in the most challenging of environments such as urban canyons, recent smartphones have integrated the existing GPS-based location platform with the Russian GLONASS system since 2011 [5] and with China's Beidou since 2013 [6]. Using this tri-band multi-constellation GNSS module, pedestrians and vehicles in urban areas can locate their positions for 95% of the day and those in harsh urban canyon (region in the red ellipse of the Figure 1) can find the locational information for 79% of the time. This is a considerable improvement over the figures of 56% and 20%, respectively, using GPS only [7]. The location chipset supporting the multi-GNSS positioning can improve the position accuracy and extend the position-available time. Table 1 presents static test results from DL-V3 (Novatel, Calgary, AB, Canada) receivers [8]. According to [PERSON]'s results, integrating GLONASS into the GPS positioning system improves the root-mean-square (RMS) and mean value of the errors by approximately 18%, and the maximum error of 9.5 m has been reduced to 6.4 m. The RMS result for GPS stand-alone error has been improved to 0.42 m by applying a differential technique, and the performance of Differential GNSS (DGNSS) is better (by approximately 15%) than that of Differential GPS (DGPS). Thus, we conclude that DGNSS can provide more accurate and robust positional solutions than DGPS. Similarly, if an adequate DGNSS solution could be applied to smartphones, we would expect the position accuracy of the multi-GNSS chipset to be improved over that of the previous GPS-only chipset.