###### Abstract
This marked the inception of a new era where flagship smartphones embraced GNSS chipsets equipped with multiple frequencies and constellations, setting a new industry standard. Contemporary research endeavors focus on advancing positioning algorithms, particularly precise point positioning (PPP) and real-time kinematic (RTK) techniques. These advancements aim to achieve new levels of accuracy in positioning with smartphones. Despite these advancements, challenges still exist due to the inherent limitations posed by smartphone hardware. The utilization of tiny non-directional linearly polarized antennae and low-power receiver chipsets in smartphones has notably reduced the quality of GNSS signals. A number of factors contribute to the intricate challenge of achieving precise positioning while using smartphones, such as low signal-to-noise ratios (SNRs), increased pseudorange noises, and poor phase continuity [5]. The impact of linearly polarized antennae on raw observations became prominently evident when the GNSS antenna signal of a smartphone was linked to an external software receiver [6]. In-depth investigations conducted by [7; 8], leveraging specially designed GNSS smartphone chipsets, showed the following concerning trend: raw observations exhibited an unusually elevated gross error ratio and remained susceptible to the detrimental influence of multipath effects. As a result of this outcome, the differential positioning accuracy achieved was confined to less than ten meters. Further exploration by [9] demonstrated the potential of time-differenced measurements, providing their capability to attain centimeter-level accuracy. This finding was particularly apparent after the duty cycle was brought to closure in the context of the Huawei P10 smartphone. Extensive evaluations, as carried out by [10; 11; 12], featured a detailed comparison of carrier-to-noise ratios (C/N0) and pseudorange noise against their geodetic receiver counterparts in the Nexus 9 tablet and Samsung S8 smartphone. These evaluations showed a substantial variation in the smartphone measurements. Refs. [13; 14] confirmed this recurring theme by demonstrating that these smartphones perform poorly in GNSS signal reception. In studies conducted by [15; 16], dual-frequency measurements were performed on the Xiaomi Mi8 smartphone, resulting in a significant breakthrough. Using L5/E5a signals, which possess extended wavelengths and high symbol rates, enabled measurements with enhanced accuracy and reduced sensitivity to multipath interference. Ref. [17] identified that the initial phase bias (IPB) calibration was insufficient in certain smartphones, including the Nexus 9, Galaxy S8, and Honor 8, resulting in markedly elevated levels of uncertainty. As a result, the importance of proper GNSS module calibration in mitigating accuracy discrepancies is evident. A thorough investigation has been conducted concerning the comprehensive analysis of observations from multi-frequency and multi-constellation smartphones. The previous studies examined the GPS L1/L5, Galileo E1/E5a, BDS B1I, and GLONASS missions. The research outcomes revealed a notable consistency in the signal power, satellite visibility, pseudorange, and carrier-phase noise. However, there remains a research void regarding the quality attributes of the data and the positioning capabilities associated with the novel signals from the BDS-3. The unavailability of raw observation data from BDS-3 in smartphones has led to a research gap in understanding these specific signals. In addressing the challenges related to multipath interference within smartphone GNSS data, diverse algorithms have been evaluated. These encompass algorithms like the CMCD (Code-Minus-Carrier Delta range) and SNR elevation-dependent methods [18]. Remarkably, implementing these techniques removed over 77% of the observed multipath effects, yielding a substantial enhancement in the accuracy of the positioning outcomes. The BCM47755 chipset, a groundbreaking development by Broadcom, marked a significant milestone in GNSSs.