Accurate and continuous outdoor pedestrian positioning using smartphones remains challenging in complex environments like urban canyons, where Global Navigation Satellite System (GNSS) signals are frequently degraded or blocked, and Pedestrian Dead Reckoning (PDR) suffers from cumulative errors. To address this, this paper proposes a novel fusion method based on a Robust Adaptive Cubature Kalman Filter (RACKF). The core of our approach is a two-stage filtering architecture: the first stage employs a quaternion-based RACKF to optimally fuse gyroscope and magnetometer data for robust heading estimation; the second stage performs the core fusion of GNSS observations with an enhanced 3D PDR solution. Key innovations include an adaptive noise estimation strategy combining fading and limited memory weighting, a robust M-estimator-based mechanism to suppress outliers, and the integration of differential barometric height measurements. Experimental results demonstrate that the proposed method achieves a horizontal positioning accuracy of 3.28 m (RMSE), outperforming standalone GNSS and improving 3D PDR by 25.97% and 10.39%, respectively. This work provides a practical, infrastructure-free solution for robust smartphone-based outdoor navigation.

Due to the increasing demand for accurate and robust GNSS positioning for location-based services (LBS) in urban regions, the impacts prevalent in metropolitan areas, like multipath reflections and various interferences, have become persistent challenges. Consequently, developing effective strategies to address these sophisticated influences has become both a primary research focus and a shared priority. In this paper, the authors explore an approach to identify and mitigate the drawbacks arising from multipath effects in urban positioning. Unlike conventional ways for building complex models, an adaptive data-driven methodology is proposed to identify the fingerprints of a multipath in GNSS observations. This approach utilizes the Fourier transform (FT) to examine code multipath and other error sources in terms of frequency, as represented by the power spectrum. Wavelet decomposition and signal spectrum methods are subsequently applied to seek traces of code multipath in multilayer decompositions. Based on the exhibited multipath features, the impacts of multipath in GNSS observations are detected and mitigated in the reconstructed observations. The proposed method is validated for both static and dynamic positioning scenarios, demonstrating seamless integration with existing positioning models. The feasibility has been verified through a series of experiments and tests under urban environments using navigation terminals and smartphones.

Ground texture-based localization leverages environment-invariant, planar-constrained features to enhance pose estimation robustness, thus offering inherent advantages for seamless localization. However, traditional feature extraction methods struggle with reliable performance under large-scale rotations and texture sparsity in the case of ground texture-based localization. This study addresses these challenges through a learning-based feature extraction framework-Ground Texture Rotation-Equivariant Keypoints and Descriptors (GT-REKD). The GT-REKD framework employs group-equivariant convolutions over the cyclic rotation group, augmented with directional attention and orientation-encoding heads, to produce dense keypoints and descriptors that are exactly invariant to 0-360° in-plane rotations. The experimental results for ground texture localization show that GT-REKD achieves 96.14% matching in pure rotation tests, 94.08% in incremental localization, and relocalization errors of 5.55° and 4.41 px (≈0.1 cm), consistently outperforming baseline methods under extreme rotations and sparse textures, highlighting its applicability to visual localization and simultaneous localization and mapping (SLAM) tasks.

With the development of positioning technology and the widespread application of mobile positioning terminal devices, the acquisition of trajectory data has become increasingly convenient. Furthermore, mining information related to scenic spots and tourists from trajectory data has also become increasingly convenient. This study used the normalization results of information entropy to evaluate the attraction of scenic spots and the experience index of tourists. Tourists and scenic spots were chosen as the probability variables to calculate information entropy, and the probability values of each variable were calculated according to certain methods. There is a certain competitive relationship between scenic spots of the same type. When the distance between various scenic spots is relatively close (less than 8 km), a strong cooperative relationship can be established. Scenic spots with various levels of attraction can generally be classified as follows: cultural heritage, natural landscape, and leisure and entertainment. Scenic spots with higher attraction are usually those with a higher A-level and convenient transportation. A considerable number of tourists do not choose to visit crowded scenic destinations but choose some spots that they are more interested in according to personal preferences and based on access to free travel.

Differential code bias (DCB) is one of the main errors involved in ionospheric total electron content (TEC) retrieval using a global navigation satellite system (GNSS). It is typically assumed to be constant over time. However, this assumption is not always valid because receiver DCBs have long been known to exhibit apparent intraday variations. In this paper, a combined method is introduced to estimate the epoch-wise receiver DCB, which is divided into two parts: the receiver DCB at the initial epoch and its change with respect to the initial value. In the study, this method was proved feasible by subsequent experiments and was applied to analyze the possible reason for the intraday receiver DCB characteristics of 200 International GNSS Service (IGS) stations in 2014 (high solar activity) and 2017 (low solar activity). The results show that the proportion of intraday receiver DCB stability less than 1 ns increased from 72.5% in 2014 to 87% in 2017, mainly owing to the replacement of the receiver hardware in stations. Meanwhile, the intraday receiver DCB estimates in summer generally exhibited more instability than those in other seasons. Although more than 90% of the stations maintained an intraday receiver DCB stability within 2 ns, substantial variations with a peak-to-peak range of 5.78 ns were detected for certain stations, yielding an impact of almost 13.84 TECU on the TEC estimates. Moreover, the intraday variability of the receiver DCBs is related to the receiver environment temperature. Their correlation coefficient (greater than 0.5 in our analyzed case) increases with the temperature. By contrast, the receiver firmware version does not exert a great impact on the intraday variation characteristics of the receiver DCB in this case.

Precipitable water vapor (PWV), as the most abundant greenhouse gas, significantly impacts the evapotranspiration process and thus the global climate. However, the applicability of mainstream satellite PWV products is limited by the tradeoff between spatial and temporal resolutions, as well as some external factors such as cloud contamination. In this study, we proposed a novel PWV spatio-temporal fusion framework based on the zero-shot super-resolution and the multivariate autoregression models (ZSSR-ARF) to improve the accuracy and continuity of PWV. The framework is implemented in a way that the satellite-derived observations (MOD05) are fused with the reanalysis data (ERA5) to generate accurate and seamless PWV of high spatio-temporal resolution (0.01 degrees, daily) across the European continent from 2001 to 2021. Firstly, the ZSSR approach is used to enhance the spatial resolution of ERA5 PWV based on the internal recurrence of image information. Secondly, the optimal ERA5MOD05 image pairs are selected based on the image similarity as inputs to improve the fusion accuracy. Thirdly, the framework develops a multivariate autoregressive fusion approach to allocate weights adaptively for the high-resolution image prediction, which primely addresses the non-stationarity and autocorrelation of PWV. The results reveal that the accuracies of fused PWV are consistent with those of the GPS retrievals (r = 0.82-0.95 and RMSE = 2.21-4.01 mm), showing an enhancement in the accuracy and continuity compared to the original MODIS PWV. The ZSSR-ARF fusion framework outperforms the other methods with R2 improved by over 24% and RMSE reduced by over 0.61 mm. Furthermore, the fused PWV exhibits similar temporal consistency (mean difference of 0.40 mm and DSTD of 3.22 mm) to the reliable ERA5 products, and substantial increasing trends (mean of 0.057 mm/year and over 0.1 mm/year near the southern and western coasts) are observed over the European continent. As the accuracy and continuity of PWV are improved, the outcome of this paper has potential for climatic analyses during the land-atmosphere cycle process.

To maintain safe and smooth operation of urban infrastructures, security evaluation and deformation monitoring have formed a challenging issue, especially for large scale buildings and structures since they are usually built within urban areas and densely populated districts. In recent years, related specific methods have been presented to achieve valid monitoring for these human structures, among which, GNSS, either combined with other spatial technologies such as remote sensing, SAR or InSAR, or sensors including inertial system or total stations, have played active roles for deformation monitoring. We propose a way to combine GNSS with wireless sensor network (WSN) to form a unique approach for deformation monitoring for some special regions that can not be effectively tackled by previous schemes. Our research work lies in two aspects. One is the configuration of networks in which roving GNSS receivers and reference receivers are properly distributed to form a more flexible configuration to suit for specific shapes of structures. The other feature is the employment of wireless sensor network to form a flexible routine for cost-effective data communication within the monitoring areas. Some methods to deal with challenges arisen from impacts like multipath and deformation prediction are also explored and discussed to promote monitoring efficiency. Related tests have been conducted to exemplify our technical routines and verify the feasibility of suggested monitoring schemes.

With multiple GNSS availability and wider applications, many positioning modes like SPS, DGNSS, RTK and PPP have been developed to cover these flexible employments. Currently, some intricate influences such as ionosphere delays and environmental interference have caused obvious impacts to accurate positioning, and they are therefore formed highlighted research points for worldwide researchers and scholars. To deal with the ionosphere delay, this submission has proposed an intrinsic approach to identify and extract the ionospheric disturbance from GNSS observations in which Digital Signal Process (DSP) is employed to form an adaptive band pass filter for combined observations in the form of either code minus phase (CMP) or double differential (DD) carrier phase. Related mechanism for identification and extraction of the ionosphere delay using DSP are introduced and analyzed. We have also discussed the separation of ionosphere delay with multipath caused by signal reflections from nearby surroundings when deformation monitoring is conducted on some restricted environments. Effects achieved by DSP have been verified combining with typical SPS and carrier based relative positioning conducted in Guangzhou metro construction sites. Results exhibit that the suggested standalone approach to extract ionosphere delays is an innovative technical frame to achieve accurate positioning regardless of assistant messages from base stations or satellite navigation broadcast.

The tracking of satellite signals with the passive linearly polarized embedded global navigation satellite system (GNSS) antenna of smartphones in dynamic scenarios is susceptible to the changing multipath and obstructions in urban environments, which lead to a significant decrease in the availability and reliability of GNSS solutions. Accordingly, based on the characteristics of smartphone GNSS and inertial measurement unit (IMU) sensors data in GNSS-degraded environments, we established an IMU-aided uncombined precise point positioning (PPP) mathematical model that is suitable for smartphones. To enhance the reliability of initial alignment in dynamic mode, the step function variances depending on carrier-to-noise density ratio were established with the variances of GNSS measurements, and the inertial navigation system (INS) parameters were initialized while both the velocity of smartphones and the position dilution of precision (PDOP) reached corresponding thresholds. Considering the measurement noise and observations gaps of smartphones, the robust Kalman filter (RKF) with equivalent variance matrix was used for parameter estimation to improve the convergence efficiency of the coupled PPP/INS model. Experimental results indicated that the proposed PPP/INS method can effectively improve the positioning performance of smartphones in GNSS-degraded environments. Compared with the conventional smartphone PPP scheme, the PPP/INS horizontal errors in the eastern and western areas of the long trajectory experiment decreased by 49.37% and 48.29%, respectively. Meanwhile, the trajectory deviation of smartphones can remain stable in the tunnel where GNSS signals are blocked.

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

In global navigation satellite system (GNSS)-based positioning and applications, multipath is by far the most obstinate impact. To overcome paradoxical issues faced by current processing approaches for multipath, this paper employs an intrinsic method to identify and mitigate multipath based on empirical mode decomposition (EMD) and Hilbert–Huang transform (HHT). Frequency spectrum and power spectrum are comprehensively employed to identify and extract multipath from complex data series composed by combined GNSS observations. To systematically inspect the multipath from both code range and carrier phase, typical kinds of combinations of the GNSS observations including the code minus phase (CMP), differential correction (DC), and double differential (DD) carrier phase are selected for the suggested intrinsic approach to recognize and mitigate multipath under typical positioning modes. Compared with other current processing algorithms, the proposed methodology can deal with multipath under normal positioning modes without recourse to the conditions that satellite orbits are accurately repeated and surrounding environments of observing sites remain intact. The method can adaptively extract and eliminate multipath from solely the GNSS observations using intrinsic decomposition mechanism. From theoretical discussions and validating tests, it is found that both code and carrier phase multipath can be identified and distinguished from ionospheric delay and other impacts using the EMD based techniques. The resultant positioning accuracy is therefore improved to an obvious extent after the removal of the multipath. Overall, the proposed method can form an extensive and sound technical frame to enhance localization accuracy under typical GNSS positioning modes and harsh multipath environments.

Pipe elbow joints exist in almost every piping system supporting many important applications such as clean water supply. However, spatial information of the elbow joints is rarely extracted and analyzed from observations such as point cloud data obtained from laser scanning due to lack of a complete geometric model that can be applied to different types of joints. In this paper, we proposed a novel geometric model and several model adaptions for typical elbow joints including the 90° and 45° types, which facilitates the use of 3D point clouds of the elbow joints collected from laser scanning. The model comprises translational, rotational, and dimensional parameters, which can be used not only for monitoring the joints' geometry but also other applications such as point cloud registrations. Both simulated and real datasets were used to verify the model, and two applications derived from the proposed model (point cloud registration and mounting bracket detection) were shown. The results of the geometric fitting of the simulated datasets suggest that the model can accurately recover the geometry of the joint with very low translational (0.3 mm) and rotational (0.064°) errors when ±0.02 m random errors were introduced to coordinates of a simulated 90° joint (with diameter equal to 0.2 m). The fitting of the real datasets suggests that the accuracy of the diameter estimate reaches 97.2%. The joint-based registration accuracy reaches sub-decimeter and sub-degree levels for the translational and rotational parameters, respectively.

The emergence of dual frequency global navigation satellite system (GNSS) chip actively promotes the progress of precise point positioning (PPP) technology in Android smartphones. However, some characteristics of GNSS signals on current smartphones still adversely affect the positioning accuracy of multi-GNSS PPP. In order to reduce the adverse effects on positioning, this paper takes Huawei Mate30 as the experimental object and presents the analysis of multi-GNSS observations from the aspects of carrier-to-noise ratio, cycle slip, gradual accumulation of phase error, and pseudorange residual. Accordingly, we establish a multi-GNSS PPP mathematical model that is more suitable for GNSS observations from a smartphone. The stochastic model is composed of GNSS step function variances depending on carrier-to-noise ratio, and the robust Kalman filter is applied to parameter estimation. The multi-GNSS experimental results show that the proposed PPP method can significantly reduce the effect of poor satellite signal quality on positioning accuracy. Compared with the conventional PPP model, the root mean square (RMS) of GPS/BeiDou (BDS)/GLONASS static PPP horizontal and vertical errors in the initial 10 min decreased by 23.71% and 62.06%, respectively, and the horizontal positioning accuracy reached 10 cm within 100 min. Meanwhile, the kinematic PPP maximum three-dimensional positioning error of GPS/BDS/GLONASS decreased from 16.543 to 10.317 m.

目前北斗卫星导航系统(BDS)服务于亚太地区,并即将向"一带一路"沿线以及周边国家提供基本服务,走向全球覆盖,在测绘、交通、监测等众多领域发挥重要作用.主要利用BD/GPS双模定位和在监测区中构建的Zigbee无线传感网络,设计了实时自动化的形变监测系统;并以某大坝为例,通过实际应用验证了其可行性.

室内定位需求急剧增加,普及的智能手机带来了解决问题的一种方法.本文提出了一种基于智能手机的粒子滤波室内融合定位方法.利用三轴加速计和三轴罗盘等微机电系统(micro-electromechinical system,MEMS)传感器数据估计目标的运动状态信息,利用WiFi数据更新运动状态,实现融合定位.室内动态环境下实验结果表明,融合定位方法平均定位误差小于2m,其有效利用智能手机平台获取多种传感器数据,很好地结合了行人航迹推算方法和K加权最近邻方法的优势,在定位精度和稳健性方面均有良好表现.

在国家一带一路倡议与实施战略的引导下,随着我国基础建设的技术水平快速提高,基础设施的建设速度快速提高,规模迅速扩大,把握与维持这些重大设施与工程的安全成为一项紧迫而又重要的任务.形变监测是这项任务的典型技术手段.本文针对形变模拟分析中常用的自回归模型(AR)的应用现状与存在问题,提出了基于相关性深化分析的自适应AR模型阶数控制方法,通过模拟结果的残差相关分析,补偿改进AR模型的有效阶数,使之更客观地反映变形序列的变化规律,扩展了序列分析与形变模拟的有效性,并且以某高楼的形变研究为例,通过实际观测数据和应用实例验证了改进模型的可行性.

针对室内及卫星信号遮蔽区域的定位问题,提出了基于粒子滤波的混合定位方法.此方法通过采集和处理WiFi和其他泛在信号,比如WSN(wireless sensor network,2.4GHz Zigbee)的混合信号强度信息实现位置估算,其中,WiFi数据作为滤波过程的主要观测量,而WSN数据作为增强信息辅助粒子重采样来提高关键区域的定位精度.在卫星信号遮蔽区域开展了实验,结果表明,与单一定位方法相比,混合定位方法取得了更高的定位精度.

Beidou Navigation System is providing services to the Asia-Pacific region, and it is gradually to achieve global coverage.It plays an important role in many fields, such as surveying and mapping, transportation, monitoring, and so on.This article discusses hybrid BD/GPS precise positioning.A deformation monitoring network is designed for the dam of Feilaixiain Qingyuan, Guangdong Province.Zigbee wireless sensor network is integrated for communication.So that, an automatic deformation monitoring system is formed.The monitoring capability of deformation monitoring network using BD/GPS and GPS positioning is compared.And results show that the BD/GPS positioning can enhanced the ability of deformation monitoring network.