To address the issue of deviations in amplitude extraction caused by unmodeled errors in global navigation satellite system interferometric reflectometry(GNSS-IR) soil moisture inversion, this study proposes a method that utilizes a semi-parametric model to introduce nonparametric vectors compensating for unmodeled errors, thereby optimizing amplitude extraction. The soil moisture inversion results based on parametric models are compared with those derived from semi-parametric models. The findings demonstrate that the amplitude extracted by the semi-parametric model achieves higher accuracy in both single satellite and multi-satellite fusion scenarios. The proposed approach effectively improves inversion precision by mitigating unmodeled errors through semi-parametric compensation.

This study investigates the issue of using different gravity field model orders for the varying altitudes of highly elliptical orbits(HEOs). Using the gravity field optimization recursive algorithm, 54 circular orbits with different orbital altitudes were integrated with gravity field models of different orders for 6 hours and their errors were statistically analyzed. Four gravity field order selection models, including polynomial, power function, rational number approximation and piecewise selection, were constructed.The four HEOs in the lunar exploration project were simulated for experimental verification. The integration time for the four models decreased from 13-15 seconds to 2-3 seconds, significantly enhancing computational efficiency, with the integration times of the four selection models being close to each other.The polynomial fitting model achieved the highest integration accuracy among the four selection models, with the accuracy of the four HEOs using the polynomial fitting model being 55.25%, 95.88%, 77.33%, and 76.23% higher than that of the piecewise selection model, respectively.

Using the real-time orbit and clock products from CNES over a one-year period, with URE(user range error) as the evaluation metric, this study analyzes the fault rates, fault types, fault sources, as well as the post-fault accuracy and precise point positioning(PPP) performance of GPS, Galileo, BDS-2, and BDS-3 by comparing them with post-processed products. The results indicate that the fault rates of GPS and Galileo are lower than those of BDS, with significant differences observed between the fault rates of BDS-3 and BDS-2. Regarding fault types, abrupt faults are the least common, while clock offsets are the primary source of faults(accounting for over 65%). After fault elimination, the mean URA(user range accuracy) values for GPS, Galileo, and BDS-3 are 0.02 m, 0.03 m, and 0.05 m, respectively, whereas there is considerable variation in URA among different satellites in BDS-2. Global station simulated kinematic PPP tests demonstrate that excluding satellite fault periods can effectively ensure positioning accuracy.

In inertial pedestrian navigation, traditional fixed-threshold detection methods often significantly increase the positioning error of pedestrians due to false positives and false negatives. To address this issue, this paper proposes an improved zero-velocity interval detection algorithm. The algorithm first constructs an adaptive threshold detection model based on the generalized likelihood ratio test(GLRT). It then uses the threshold values obtained from multi-constraint detection to eliminate false positives in the adaptive detection process, thereby improving the detection accuracy of zero-velocity intervals. To verify the performance of the algorithm, two experimental scenarios were designed: constant-speed and fast walking along L-shaped and rectangular routes. The experimental results show that compared with acceleration magnitude variance(AMV) detection and GLRT detection, the proposed algorithm is not only simple to implement but also reduces the root mean square error(RMSE) of reference points by more than 40%, significantly enhancing the accuracy of inertial pedestrian navigation.

A combined positioning method that integrates ultra wide band (UWB) with low-cost micro-electro-mechanical systems (MEMS) inertial measurement unit (IMU) is proposed. Initially, UWB ranging data is optimized using ranging bias correction and Kalman filtering. Subsequently, the complementary strengths of multiple sensors are leveraged through a UWB/IMU fusion model. Experimental results demonstrate that the optimized UWB achieves an improvement in average positioning accuracy from 15.70 cm to 13.75 cm, with the RMSE decreasing from 22.45 cm to 19.70 cm. In non-line-of-sight (NLOS) scenarios, even when UWB signals experience abrupt changes or are lost, the fused system can still provide continuous and reliable positional information, with an average accuracy of 13.23 cm and an RMSE of 17.90 cm.

Existing research on GNSS signal classification primarily targets professional-grade receivers, distinguishing between line-of-sight(LOS) and non-line-of-sight(NLOS) signals through the extraction of signal features to achieve high-accuracy positioning. However, the signal features and classification methods used in these studies may not be applicable to the signal characteristics of low-cost GNSS chips in smartphones. This paper proposes a machine learning-based method for classifying smartphone GNSS reception signals into LOS and NLOS. The method combines five key indicators: carrier-to-noise ratio, elevation angle, pseudorange residuals, normalized pseudorange residuals, and pseudorange residual percentage. It also integrates deep learning-based zenith view semantic segmentation and satellite calibration techniques to construct an accurate training dataset. Evaluations of various machine learning models in typical urban scenarios show that the proposed method achieves an average classification accuracy of 85.8% across different smartphone models.

To simulate the impact of terrain on co-seismic deformation, this paper established three different terrain models. The results show that compared with flat terrain, convex terrain reduces the amount of horizontal displacement, while concave terrain significantly increases the amount of horizontal displacement. The terrain difference in the epicentral area of the 2024 Wushi, Xinjiang M_W7.0 earthquake is significant, and the impact of the real terrain on the co-seismic deformation of this earthquake is not yet clear. This paper uses the spectral element method to simulate the co-seismic deformation of the Wushi earthquake and compares it with the co-seismic displacement field of D-InSAR. The results indicate that the impact of terrain factors on co-seismic deformation can reach 8.5%, 26.9%, and 6.8% in the E, N, and U directions, respectively.

We analyzed the Dingri M_S6.8 earthquake as a case study. A multivariate dataset comprising land surface temperature, net surface solar radiation, and other variables in the study area was constructed. After decomposing the time series of each variable, the generalized extreme studentized deviate(GESD) algorithm was applied to extract anomaly signals. The weighted averaging method was then employed to integrate multi-variable anomaly extraction results. The results indicate that the thermal infrared anomalies in the half-year period before the Dingri M_S6.8 earthquake exhibited a cyclic change pattern of "emergence-enhancement-weakening". The Dingri M_S6.8 earthquake occurred 100 days after the maximum value of the anomalies appeared, and the main direction of the anomalies was NW-SE, forming an approximately 45° angle with the Dengmo Co fault, which is the seismogenic structure of this earthquake.

Using Sentinel-1A data, this study employed D-InSAR technology to obtain the line-of-sight (LOS) co-seismic deformation field of the M_S6.8 earthquake in Dingri, Xizang, on January 7, 2025. The geometric parameters of the seismogenic fault were derived through a Bayesian approach. Fault slip distribution was inverted using the SDM, and co-seismic Coulomb stress changes were calculated. The inversion results indicate that the seismogenic fault has a strike of 187.34°, a dip angle of 56.40°, with an epicenter located at 87.50°E and 28.64°N, at a depth of 6.81 km. The maximum slip in the co-seismic slip distribution is 4.24 m, with an average rake angle of-58.82°, suggesting that the earthquake was predominantly a normal faulting event with a minor component of left-lateral strike-slip. The inverted moment magnitude is M_W7.0, and the seismogenic fault is preliminarily identified as the Dengmo Co fault. Additionally, regions along the Zanda-Lhazê-Qiongduojiang fault, Darjeeling-Ngamring-Rinbung fault, and parts of the south xizang detachment system fault, as well as the southern sections of the Xainza-Dingjie fault and Dengmo Co fault, exhibit Coulomb stress increases exceeding 0.1 bar, indicating that these areas warrant greater attention for future seismic hazards.

In this paper, the geometry, kinematics and chronology of Shenjia-Guanzhuang ductile shear zone on the eastern margin of Dabie orogenic belt are studied in detail by means of field investigation, laboratory study and zircon U-Pb dating, and the geological significance of shear activity duration is discussed. The results showed that: The Shenjia-Guanzhuang ductile shear zone changes from NNW-SSE to SWW-NEE from west to east, and its tendency rotates counterclockwise from NEE to NNW, and its inclination gradually increases from about 40° to about 55°. Its kinematic characteristics change from forward sliding overlying shear to right lateral translacing shear. The overall performance is a "bow" shape with SW protrating, and the upper plate moves towards NEE. The start-up time is not earlier than 129.3±2.5 Ma, and the end is before 124.7±1.7 Ma. The end of ductile shear activity marks the end of intracontinental orogenic evolution of Dabie orogenic belt, and the beginning of post-orogenic uneven block uplift stage. The tectonic regime transformation of Dabie mountains and even eastern China has been completed, and the Dabie mountains have entered the coastal Pacific tectonic domain, which may be closely related to the major adjustment of the movement mode of the Izanagi plate in the western Pacific.