Under climate change, conventional droughts that accumulate over long periods and flash droughts that intensify within weeks increasingly co-occur, yet existing drought indices typically focus on a single aspect of the hydrological system and cannot simultaneously resolve long-term cumulative deficits and short-term rapid intensification. To address this limitation, we adopt a meteorological–hydrological composite drought index (GP-MSDI), which integrates terrestrial water storage (TWS) inverted from Global Navigation Satellite System (GNSS) observations with precipitation data through a Copula joint-probability model, and couple it with a three-dimensional spatiotemporal connectivity algorithm to establish a unified framework for identifying both conventional and flash drought events at monthly and weekly scales. Applying this framework to the western United States from 2005 to 2024, we find that GP-MSDI agrees closely with the U.S. Drought Monitor (USDM) in both temporal evolution and spatial propagation. A total of 12 conventional and 64 flash drought events are identified, with the peak spatial extent of two megadroughts almost covering the entire study area. Approximately 39% of the flash droughts evolved into prolonged drought events. The frequency and persistence of flash droughts are strongly modulated by the long-term hydrological background. The three megadrought episodes each coincided with phases of simultaneously negative Niño 3.4 and Pacific Decadal Oscillation (PDO) indices. The proposed framework is transferable to other regions equippedwith dense GNSS networks where integrated drought products comparable to USDM are not available, providing methodological support for regional water-resource management and drought-risk assessment.

Currently, the Global Navigation Satellite System multipath reflectometry (GNSS-MR) technique is limited in terms of temporal resolution for use in retrieving water levels. To address this issue, a frequency-adaptive window Lomb-Scargle periodogram (F-adaptive winLSP) method is proposed to dynamically determine the optimal window width of the signal-to-noise ratio (SNR) residual sequence according to the multipath signal frequencies. The proposed F-adaptive winLSP method demonstrates the capability to substantially improve the temporal resolution of retrieval results without sacrificing accuracy. Experimental results indicate that the number of valid data points of the results increased significantly by F-adaptive winLSP method during the frequency rising phase at coastal station, with improvements of 70.94% and 25.31%, respectively, compared to that by Time winLSP and Elevation winLSP. During the frequency declining phase, the retrieval accuracy is improved 21.95% and 14.30% by F-adaptive winLSP method, compared to the other two methods. Additional experiments conducted at a dam station further underscore the superior performance of the proposed method. It achieves the highest temporal resolution, valid data points of the results increasing by 873.28%, 96.24%, and 121.57% compared to the classical LSP method, Time winLSP method, and Elevation winLSP method, respectively. Moreover, the proposed F-adaptive winLSP method also achieves the highest accuracy, and it is more preferable than the other three methods for GNSS-MR water level variation retrieval, especially for increasing the number of retrievals to enhance its temporal resolution.

The integrated navigation system frequently degrades to an inertial navigation system (INS) standalone in complex urban environments due to recurring signal interruptions of the Global Navigation Satellite System (GNSS). Nonholonomic constraints (NHCs) and odometry assistance can effectively suppress the accumulation of INS errors during GNSS outages. However, traditional NHC requires precise compensation for inertial measuring unit (IMU) installation angles, while odometry is inaccessible in many applications. To address these challenges, we propose a novel velocity hybrid regression network (VHRNet), which integrates the superiority of convolutional neural network (CNN) and Transformer for IMU data mining. This network establishes a mapping between IMU outputs and virtual NHC and odometry measurement to apply 3-D velocity full constraints without IMU installation angles compensation and hardware-wheeled odometry. Moreover, a low-complexity adaptive optimization model in the variance domain is introduced, which can adaptively adjust noise covariance to mitigate the impact of gross errors and frequent stopping events. Land vehicle experiments demonstrate that VHRNet effectively improves the prediction accuracy of 3-D velocities, achieving 0.028, 0.562, and 0.034 m/s in lateral, forward, and vertical directions, respectively. Compared to classic time series prediction models, the prediction accuracy for the 3-D velocity improves by more than 12.5%, 21.7%, and 17.0%. In various GNSS outage scenarios, the positioning performance of the pseudo-3-D velocity constraint method significantly surpasses the traditional method with precise installation angle compensation. Notably, when GNSS outages for 120 s, positioning accuracy improves by 87.3% compared to single INS. In addition, the implementation of a noise-adaptive model yields more precise and robust positioning results.

Limited by the number and location distribution of ground stations, the ground based ionospheric detection using Global Navigation Satellite Systems (GNSS) technology suffers from low accuracy and poor reliability in some regions. To improve the performance of the global ionospheric model, this study combines the Continuously Operating Reference Station and the Low Earth Orbit (LEO) Satellite observation to conduct the ground and space-based (G/SBased) joint ionospheric detection technique. With reference to the ionospheric radar data of the global ionospheric radio observatory, the performance of the G/SBased GNSS joint ionospheric detection technique was tested in quiet and disturbed geomagnetic environments. Results show that in the ground and space based joint mode, the distribution of ionospheric puncture points (IPPs) is uniform, thus effectively avoiding the problem of IPPs blank in the absence of ground stations. In the quiet geomagnetic environment, the ionospheric detection of the G/SBased joint mode has a remarkable performance improvement compared with the ground GNSS mode, with an average improvement of 55.51%. In the geomagnetically disturbed environment, the G/SBased joint mode still has high consistency with the ionospheric radar results, and the detection performance is better than that of the ground GNSS mode. This research shows that combining with ground GNSS and LEO satellite observation data can substantially provide the performance of GNSS ionospheric total electron content detection and can provide good data support for the construction of a global ionospheric refinement model.

The gravity–geologic method (GGM) is widely used for bathymetric predictions. However, the conventional GGM cannot be applied in regions without actual bathymetric data. The modified gravity–geologic method (MGGM) enhances the accuracy of bathymetric models by supplementing short-wavelength gravity anomalies with an a priori bathymetric model, but it overlooks the significance of actual bathymetric data in the prediction process. In this study, we used the BP neural network (BPNN), incorporating shipborne depth soundings and coastline data as zero-depth estimates combined with the MGGM to produce a bathymetric model (BPGGM_BAT) for the South China Sea (105°E–122°E, 0°N–26°N). The results indicate that the BPGGM_BAT model decreases the root-mean-square (RMS) of bathymetry differences from 154.33 m to approximately 140.43 m relative to multibeam depth data. Additionally, the RMS differences between the BPGGM_BAT model and multibeam depth data show further improvements of 19.63%, 20.10%, and 19.54% when compared with the recently released SRTM15_V2.6, GEBCO_2022, and topo_V27.1 models, respectively. The precision of the BPGGM_BAT model is comparable to that of the SDUST2023BCO model, as verified using multibeam depth data in open sea regions. The BPGGM_BAT model outperforms existing models with RMS differences of 8.54% to 32.66%, as verified using Electronic Navigational Chart (ENC) bathymetric data in the regions around the Zhongsha and Nansha Islands. A power density analysis suggests that the BPGGM_BAT model is superior to the MGGM_BAT model for predicting seafloor topography within wavelengths shorter than 15 km, and its performance is closely consistent with that of the topo_V27.1 and SDUST2023BCO models. Overall, this integrated method demonstrates significant potential for improving the accuracy of bathymetric predictions.

Anomalies caused by the failure of ranging-related facilities and satellite orbit maneuvering will greatly affect the performance of Autonomous Orbit Determination (AOD) for Global Navigation Satellite System (GNSS). In view of that, we proposed an improved robust filtering named as Resisting Anomaly Robust Filtering (RARF) to improve the precision and enhance the reliability of AOD in situation of anomalies in observations and states of satellites (i.e. orbit maneuvering). We performed the centralized AOD with the RARF for a hybrid GNSS constellation using simulated observations, and analyzed its performance in cases of no anomaly, anomalies in observations only and anomalies in states of satellites only. The experimental results indicate that: (1) With anomalies in observations only, the RARF is much more robust than the extended Kalman filter (EKF), and results of AOD with the RARF are entirely free from abnormal observations; (2) In the situation of anomalies in states of satellites only, the precision of AOD with the RARF can reach to the order of 10 m after 1.5 h orbit recovery if an anchor is available. As the solving time extends, the precision of AOD can be up to dm level. (c) 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

Fast and reliable cycle slip determination can ensure successive ambiguity resolution and precise positioning. Generally, it is not difficult to determine big cycle slips using the linear combination of observations, for instance, the geometry-free (GF) combination and the Hatch-Melbourne-Wubbena (HMW) combination. However, the participation of pseudorange observations may fail to identify small cycle slips. This contribution proposes an integration scheme combining the improved geometry-free (IGF) combination and time-difference carrier phase (TDCP) model to determine simultaneous cycle slips for undifferenced kinematic data. The IGF combination, which is improved from the modified geometry-free (MGF) combination and utilizes the Gauss floor function to take the decimal part of GF, can directly determine small cycle slips on a specific frequency. The TDCP model is used to estimate the differences of position and clock error between adjacent epochs by least-square adjustment using clean phase observations, and repair the remaining cycle slips by the predicted TDCP measurements obtained from the predetermined parameters. The proposed method is tested against 1 Hz kinematic dataset with simulation and highway dataset with real cycle slips. In the simulation test, all cycle slips can be correctly repaired by IGF for L1/L5, E1/E5 and E1/E5a. These combinations are unavailable for MGF. Compared to the HMW-GF method, the repair success rate of IGF improves from 96.80% to 99.16%, and the number of incorrect cases reduces from 9650 to 2535. The IGF-TDCP integration scheme can further improve the performance of IGF, whose repair success rate is more than 99.97% and incorrect cases are 38. The highway test shows that the proposed method effectively processes simultaneous cycle slips on more than half of the tracking satellites caused by the overpasses, even in the case of simultaneous 3 second data gaps on 5 satellites of 6 tracking satellites.

The tropospheric Zenith Wet Delay (ZWD) is one of the primary sources of error in Global Navigation Satellite Systems (GNSS). Precise ZWD modeling is essential for GNSS positioning and Precipitable Water Vapor (PWV) retrieval. However, the ZWD modeling is challenged due to the high spatiotemporal variability of water vapor, especially in low latitudes and specific climatic regions. Traditional ZWD models make it difficult to accurately fit the nonlinear variations in ZWD in these areas. A hybrid deep learning algorithm is developed for high-precision ZWD modeling, which considers the spatiotemporal characteristics and influencing factors of ZWD. The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined in the proposed algorithm to make a novel architecture, namely, the hybrid CNN-LSTM (CL) algorithm, combining CNN for local spatial feature extracting and LSTM for complex sequence dependency training. Data from 46 radiosonde sites in South America spanning from 2015 to 2021 are used to develop models of ZWD under three strategies, i.e., model CL-A without surface parameters, model CL-B with surface temperature, and model CL-C introducing surface temperature and water vapor pressure. The modeling accuracy of the proposed models is validated using the data from 46 radiosonde sites in 2022. The results indicate that CL-A demonstrates slightly better accuracy compared to the Global Pressure and Temperature 3 (GPT3) model; CL-B shows a precision increase of 14% compared to the Saastamoinen model, and CL-C exhibits accuracy improvements of 30% and 12% compared to the Saastamoinen and Askne and Nordius (AN) model, respectively. Evaluating the models’ generalization capabilities at non-modeled sites in South America, data from six sites in 2022 were used. CL-A shows overall better performance compared to the GPT3 model; CL-B’s accuracy is 19% better than the Saastamoinen model, and CL-C’s accuracy is enhanced by 33% and 10% compared to the Saastamoinen and AN model, respectively. Additionally, the proposed hybrid algorithm demonstrates a certain degree of improvement in both modeling accuracy and generalization accuracy for the South American region compared to individual CNN and LSTM algorithm.

随着BDS-3的升空运行,GNSS多星座观测条件得到显著改善,面对新一代北斗卫星导航系统,TB 10601—2009《高速铁路工程测量规范》的控制网观测时长以及作业效率的性能有待评估分析.首先,根据单GPS、GPS+BDS卫星可见性分析全球PDOP值的变化情况;然后,推导其基线解算精度随观测时长变化的先验模型;最后,以某高铁线路CPⅡ控制网的观测数据为例,通过自编软件实现单BDS、单GPS、GPS+BDS三种模式的基线解算.结果显示,相比单GPS、GPS+BDS在我国的PDOP值能够降低0.7～1.6,且相对定位精度(观测时长约16 min)与单GPS(观测时长60 min)相当.示例结果中,GPS+BDS平面和高程方向定位精度分别为4.3 mm和10.9 mm,相比单GPS分别有54％和57％的提升.结果表明,在高铁CPⅡ控制网测量中,GPS+BDS能够将观测时长缩短为规范的34％,且满足GPS所要求的指标,并对规范修订具有一定参考价值.

Accuracy and resolution are the two primary challenges that impose limitations on the practical implementation of classical tide-level remote sensing. To improve the accuracy and applicability and increase the temporal resolution of the inversion point near the shore area, the influence of coastal reflection signals in the signal-to-noise ratio (SNR) residual sequence should be weakened significantly. This contribution proposes an anti-interference GNSS Multipath Reflectometry (GNSS-MR) algorithm called VMD_SNR, which is enhanced using variational mode decomposition (VMD). Compared with wavelet decomposition and empirical mode decomposition (EMD) methods, VMD_SNR exhibits superior capabilities in reducing the interference caused by noisy signals. The measurements of ground-based GNSS stations are used to verify the performance improvement in the VMD_SNR algorithm. The results show that the proposed algorithm is better than the wavelet decomposition method and EMD method in terms of accuracy and stability in the shore area, where the effective number is higher than 99% of the total number, and the accuracy is better than 13.80 cm. Moreover, the accuracy improvement is more significant in the high-elevation range, which is 30.16% higher than the wavelet decomposition method and 38.34% higher than the EMD method.

The stable operation of the wireless power transmission system is inseparable from the data transmission technology. In this paper, a new method based on Orthogonal Frequency Division Multiplexing (OFDM) technology is proposed to realize the reverse simultaneous transmission of data and power on account of the problems on coupling interference and low spectrum utilization in the shared channel transmission of data and power. The power carrier is equated as the data carrier loaded with all 1 information, data is decoupled synchronously and transmitted reliably at high speed by using OFDM technology and the crosstalk generated by the power transmission process to the data transmission process can be reduced in this method. In order to stabilize the output voltage when the load varies within a certain range, the Series LCC Circuit (S/LCC) compensation topology is adopted by the power transmission channel. As a shared channel for data and power transmission, loosely coupled transformer could simultaneously and reversely transmit two different frequency carriers of data and power. The structure of the system and the basic principle of OFDM are firstly introduced in this paper; secondly, mathematical modeling of the system is carried out to analysis the transmission characteristics; and then the design methods of data modulation and demodulation are given; finally, an experimental platform with 20 W power and 85 kbit/s data transmission has been built to verify the validation of the proposed method.

Demand for the wireless power transmission system with real-time and full-duplex data transmission, a high-frequency carrier injection full-duplex simultaneous wireless power and data transfer (SWPDT) system is proposed. An LCC/CLC compensation structure is applied in the power transfer channel to achieve voltage stabilization and reduce the power interference on the data transmission. An LC parallel branch is utilized in the data receiving circuit to suppress ipsilateral data source crosstalk in full-duplex communication. This article theoretically demonstrates the voltage gain characteristics of the power and data transfer channels. Then, the crosstalk between bidirectional data transmission is discussed, along with the analysis of the power effect on the data transmission. In addition, the parameter design method is presented to optimize the data transmission gains while suppressing interference and crosstalk. Finally, the experimental prototype with 20 W output power, 250 kbps forward data rate, and 170 kbps backward data rate is built to verify the correctness and effectiveness of the proposed full-duplex SWPDT system.

针对当全球卫星导航系统(GNSS)信号失锁时,GNSS与视觉里程计(VO)组合定位方法定位精度下降的问题,提出一种基于长短期记忆(LSTM)神经网络辅助的GNSS/VO组合定位方法:在GNSS工作正常情况下,利用视觉里程计的位移增量和姿态构建LSTM的特征向量,将GNSS解算的位置增量作为输出对LSTM神经网络进行训练;GNSS信号失锁环境中,使用LSTM神经网络输出结果推算得到伪GNSS观测值,并将其与VO的结果进行松组合,实现GNSS/VO组合定位.实验结果表明,在 GNSS信号丢失 30、60、120 s的过程中,所提方法的定位精度可分别提高约 62％、64％、69％,证明该方法能够有效地提高GNSS/VO组合定位方法在GNSS拒止环境下的定位精度.

针对全球导航卫星系统(global navigation satellite system,GNSS)高频数据周跳的高可靠性快速探测与修复,基于改进的相位几何无关(modified geometry-free,MGF)组合量,提出了 MGF周跳解算的质量控制方法,以进一步提高周跳修复的成功率.根据MGF组合观测量及误差特征,推导了其周跳修复的边界条件,并分析了不同导航卫星系统MGF方法的适用性,及观测噪声对周跳解算结果的影响.在此基础上,构建了 MGF组合的质量控制检验量,经多系统GNSS实测数据试验分析,结果表明:MGF方法能够对小周跳(如小于5周的周跳)实现快速探测,修复成功率可达到99.90％以上;利用MGF组合的质量控制检验量能够分别识别出GPS、BDS和GLONASS中100％、87.57％和77.42％的错误周跳解算结果,使得MGF周跳解算错误率降低至0.01％.MGF方法受相位噪声影响显著,MGF组合的质量控制检验量能够有效识别错误的周跳解算结果,从而提高周跳修复成功率,但随着相位几何无关组合量噪声水平的增加,MGF质量控制检验量对于周跳修复错误和观测噪声的区分性降低.

The multikernel least mean square (MKLMS) algorithm is a classical algorithm of multikernel adaptive filters due to its simplicity. However, the linear growth network structure is a main challenge of MKLMS. To address this issue, a novel multiple random features least mean square (MRFLMS) algorithm is proposed by approximating multiple Gaussian kernels with the multiple random features method. In addition, a combined weight transfer strategy is adopted in MRFLMS to develop another combined multiple random features least mean square (CMRFLMS) algorithm to alleviate the influence of step-size on filtering performance and convergence rate. CMRFLMS with a fixed dimensional network structure can provide comparable performance and faster convergence rate than MKLMS. Simulations on prediction of synthetic and real non-linear system identification illustrate the superiorities of the proposed CMRFLMS algorithm from the aspects of filtering accuracy, convergence rate, and tracking performance.

In order to ensure the positioning performance of virtual reference station (VRS) based network RTK, reduce the loading impact of massive concurrent users on the solution server, and improve the service quality of VRS, this paper systematically proposed a VRS gridding technique for massive concurrent user services, and established two methods for VRS generation, namely the coordinates and triangulated irregular network (TIN) VRS grid. Considering that the key problem of space-related atmospheric delay correction is the influence of ionosphere delay, the experimental tests are conducted in the CORS convergence of middle and low latitudes respectively. The effective service spacing of gridded VRS under different regional conditions is analyzed and determined, the results show that the effective service spacing limits of grid VRS in the middle latitude and low latitudes are 12 and 7 km, respectively. Compared with the non-grid VRS (built with SPS results), the VRS generation and computing load can be greatly reduced, and the server's service capabilities to massive users can be significantly improved.

北斗三号全球卫星导航系统的建成,显著改变了多星座GNSS(全球导航卫星系统)的空间结构,为了定量评估融合北斗三号的多星座GNSS中长基线解算的精度和可靠性,采用实测中长基线数据,对不同系统组合、不同定权方法、不同观测时长下的BDS/GPS/GLONASS基线解算性能展开试验研究.在分析多星座GNSS基线解算函数模型和先验观测值定权方法的基础上,通过观测某高速铁路框架网65.9 km基线数据,进行不同策略下基线解算对比试验,结果表明:(1)BDS定位性能整体上优于GPS,0.5 h观测时长高度角定权策略下,融入BDS后三系统基线解精度比GPS/GLONASS双系统提高39.7％;(2)高度角定权法总体稳定性优于信噪比定权与等权方法,融入北斗后三系统0.5h观测时长的基线解算精度与单GPS 2h的解算精度相当;(3)融入北斗后,BDS/GPS/GLONASS三系统融合可减弱不同定权方法的中长基线解算精度的差异,水平和垂向差异分别为1 mm和5 mm.北斗三号系统将有利于铁路框架网控制测量的定位精度提升及后续作业流程优化.

In some practical applications of low-power coupled resonant wireless power transmission, besides the transmission efficiency, the anti-radial offset ability of the transceiver coils is also an important parameter to measure system performance. Consider that the positive correlation between the transmission efficiency and coupling coefficient of the coils, the symmetrical double-layer circular coils structure is proposed under the condition of maximizing the coupling coefficient in this paper. Based on the finite element method, the Maxwell simulation software is used to analyze different models. Simulation shows that the double-layer structure which reduces the number of outer layer turns can improve the coupling coefficient and the ability to resist radial offset both. It provides an idea for improving the system transmission efficiency and anti-radial offset ability both.

高斯和滤波可利用高斯混合模型精化非高斯嗓声随机模型来提高估计精度,但导航测量环境的动态性和复杂性使非高斯噪声具有时变性特征,若GMM不随之调整会导致滤波解算失真.针对该问题,本文提出一种基于位移参数自适应估计的高斯和滤波算法.首先分析GMM位移参数对非高斯噪声拟合精度的影响,然后利用位移参数自适应技术修正GMM,进而改善高斯和滤波性能.实验结果表明,当GNSS/SINS量测模型存在时变非高斯嗓声时,本文算法的滤波结果较传统高斯和滤波算法的波动小,抗干扰能力强,在实际应用中可进一步改善估计精度和稳定性.

为了解复杂地形条件下天顶对流层延迟(ZTD)特性并研究其精细化建模,采用四川省56个CORS 站的数据,解算其高精度ZTD 并对3 种不同ZTD模型的精度进行评估,分析了ZTD 时空分布特性受地形条件、季节因素的影响.基于ERA-Interim再分析数据建立了区域ZTD 格网模型及其精化方法.实验结果表明:1)ZTD 随地形分布的变化幅度大,模型的偏差分布也呈现出显著的不一致性,其中Saastamonien模型非常适合四川地区ZTD 建模,RMS和BIAS 分别为4.6、3.9 cm;2)ERA-Interim 再分析数据建立的四川区域的格网模型ZTD 与GNSS ZTD 的偏差均值仅为0.6 cm,相较Saastamonien 模型5 cm 的偏差均值,有显著改进,两者具有良好的一致性;3)通过对四川区域的格网模型ZTD 和GNSS 实测ZTD 的偏差使用精细化建模改进,格网模型的平均偏差由1.9 cm 缩减至0.5 cm.