Subsequently, a series of high precise positioning algorithms based on the raw Xiaomi Mi 8 GNSS observations were carried out. Conventional dual-frequency (ionosphere-free combination of pseudorange and carrier phase) and single-frequency precise point positioning (PPP) with raw GPS + Galileo observations from Xiaomi Mi 8 showed that, in the static open environment, decimeter-level positioning accuracy was obtained after about 102 min. In the kinematic tests, the positioning tracks had severe fluctuations of approximately 3-5 m with respect to the reference solutions [21; 22]. Surprisingly, the single-frequency PPP provided a better solution than the dual-frequency one [16; 21]. After analyzing the characteristic of raw pseudorange and carrier phase observations, a modified PPP strategy was proposed by estimating separate clock biases for pseudorange and carrier phase observations [23]. The static PPP experiments with raw GPS, Beilou, and Galileo data from Xiaomi Mi 8 under an open environment showed that the positioning errors in the north and east directions would converge to within 1 m in 30 s [23]. When conducting RTK with raw GPS, Galileo, and GLObal Navigation Satellite System (GLONASS) observations from Xiaomi Mi 8 under complex dynamic environments, the horizontal accuracy of 1.17 m was obtained but the solution tracks were still severely fluctuated [16]. To sum up, previous positioning experiments with raw GNSS measurements under open static environments could achieved decimeter-level positioning accuracy. Yet in complex urban environments, since buildings and trees have severe occlusion to GNSS signals, the frequent signal interruptions and ambiguity re-initialization cause degradation of the availability of the PPP and RTK; additionally, the positioning trajectories are unsmooth and have severe fluctuations. In addition, even though Xiaomi Mi 8 outputs dual-frequency measurements, traditional ionosphere-free combination may not always provide the best solution. Therefore, further researches on how to achieve continuous and smooth results in complex urban environments based on the raw smartphone GNSS measurements and how to make full use of the high-quality L5/E5 measurements to improve navigation accuracy are needed. In this paper, we focus on the standalone smartphone navigation problem in GNSS-degraded environments. We assume only the broadcast ephemeris is available, and there are no communications to obtain measurement data from reference stations or the State Space Representation (SSR) product from analysis centers. We use the Xiaomi Mi 8 smartphone as the navigation tool. As such, the characteristic of raw multi-GNSS observations from Xiaomi Mi 8 under static open and dynamic complex environments is assessed in terms of not only C/N0, noise of pseudorange, and carrier phase observations, but also the approximate percentage of pseudorange gross errors and carrier phase cycle slips. Then, considering the frequent signal interruptions and carrier phase cycle slips of raw Xiaomi Mi 8 observations under a complex navigation environment, an improved time-differenced navigation algorithm using raw dual-frequency multi-GNSS measurements from the smartphone is proposed. In particular, the method of properly using high-quality L5/E5 observations is discussed. Dedicated experiments under static-open and dynamic GNSS-degraded environments are carried out to verify the effectiveness of the algorithm and analyze the possibility of using raw GNSS from Xiaomi Mi 8 to achieve meter level continuous and smooth navigation in an urban environment. The rest of this paper is organized as follows. Section 2 describes the experimental data collection under static open-sky and dynamic GNSS-degraded environments. In Section 3, the characteristics of raw dual-frequency and multi-GNSS observations of Xiaomi Mi 8 in terms of not only C/N0, pseudorange, and carrier phase errors, but also the percentage of pseudorange gross errors and carrier phase cycle slips are discussed.