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The PPP-RTK system, which is capable of providing a centimeter-level real-time positioning service for an unlimited number of users, is becoming a promising tool in mass-market applications such as smartphones, the Internet of Things (IoT), and the automotive industry. The extended Kalman filter (EKF) is the conventional method for parameter estimation in the existing PPP-RTK system. Recently, an alternative method known as factor graph optimization (FGO), which fully leverages the time correlation among current and historical measurements, has the potential to further improve the accuracy and robustness of PPP-RTK solutions. In this contribution, a factor graph optimization-based PPP-RTK framework is developed, where raw pseudorange, phase measurements, precise atmospheric corrections, and time-differenced carrier-phase (TDCP) measurements serve as factors in FGO estimators. The continuously tracked phase ambiguities are estimated as the time-invariant state node and propagated by marginalization while ambiguity resolution is conducted independently between epochs. A second optimization process with the utilization of ambiguity-resolved solutions and time-differenced carrier-phase (TDCP) measurements is conducted to further improve the reliability of positioning results. The effectiveness of the proposed method is evaluated by vehicular tests in urban environments. Results indicate that the FGO method could improve the performance of ambiguity resolution by reducing the ambiguity search space and increasing the ratio values, leading to a significant accuracy improvement of 55% in an open-sky environment compared to the traditional EKF-based method. Furthermore, in GNSS signal partly block scenes, the FGO-based PPP-RTK is capable of obtaining more robust and accurate positioning solutions with fewer outliers compared to the EKF method. [ABSTRACT FROM AUTHOR]

Recently, the factor graph-based global navigation satellite systems (GNSS) positioning methods have attracted much attention for more robust positioning performance in urban canyons compared with the traditional filter-based method. In the existing factor graph optimization (FGO)-based GNSS positioning methods, the pseudorange and Doppler measurements are mainly utilized to construct factors among consecutive GNSS epochs for outlier resisting and robust localization. However, the potential of high-precision positioning by using carrier phase observations is not fully explored. A factor graph optimization-based multi-GNSS real-time kinematic (FGO-RTK) framework is proposed to fill this gap, aiming to realize robust and precise positioning in urban canyons. In our method, a sliding window-based FGO estimator is designed, in which the continuously tracked double difference ambiguities are used to establish the ambiguity constraints of position states that have common-view satellites within the window. A marginalization-based carrier phase ambiguity propagation (AP) method is developed to fully use the information of carrier phase observations for achieving more reliable and continuous ambiguity resolution. Experiments in both medium urban and deep urban environments verified the effectiveness of the proposed method. Results show that FGO-RTK (with or without the AP) can achieve centimeter-level positioning accuracy in medium urban situations, showing comparable performance with the traditional extended Kalman filter (EKF)-based RTK. With the GNSS observation conditions deteriorating in deep urban environments, FGO-RTK without AP cannot outperform EKF-RTK. By contrast, the proposed FGO-RTK, considering the AP, achieves significant improvements in centimeter- to decimeter-level positioning availability, and the 3D positioning accuracy is improved by 69.6%, compared with the EKF-RTK. Furthermore, by analyzing the impact of the window size against the performance of FGO-RTK, we found that in comparison to the FGO-RTK only using the carrier phase observations inside a fixed-size window, the proposed AP method can significantly reduce the dependence on window size, and the optimal window size can be reduced from 10 to 4 epochs with more than 50% optimization time decreasing. [ABSTRACT FROM AUTHOR]

Saline-sodic soil is widely distributed around the world and has induced severe impacts on ecosystems and agriculture. Biomass pyrolysis fluid (BPF), as a substance rich in organic acids, has been proposed as a saline-alkali soil conditioner. One of the main problems with BPF applications is the potential contamination of the phenolic substances it contains. Therefore, the purpose of this study is to reduce the phenolic substances in BFP and study the improvement effect of BFP on saline-alkali soil. Firstly, we explored the physicochemical properties of BPF prepared at different temperatures (300 °C, 400 °C, 500 °C, 600 °C, and 700 °C). Then BPF was separated into upper phases (UP) and lower phases (LP) by a simple one-step salting-out extraction method. We found that phenolic substances were mainly concentrated in the UP (average content was 193.27 mg/g), and the content of phenolic substances in the LP was effectively reduced (average content was 64.52 mg/g). Next, we added the LP diluted at different times (0, 50, 100, 200, 400) into saline-alkali soil for improvement experiments. The experimental results show that the lower phase diluted 400 times at the pyrolysis temperature of 500℃ was added into saline-alkali soil, which greatly increased the content of soil available nutrients. Under the action of organic acids, soil pH (the average was 7.43) and total salt content could be reduced effectively, and soil enzyme activities can be increased. Microbial community analysis showed that the addition of LP could increase the proportion of Actinomycetes, which played a beneficial role in improving soil fertility and then improved the growth of Chinese cabbage. [ABSTRACT FROM AUTHOR]